This document discusses using neural networks to optimize an intelligent agent's constraint satisfaction module for assigning sailors to new jobs. Various neural network techniques are evaluated, including single-layer perceptron, multilayer perceptron, and support vector machine models. The data comes from Navy databases and expert surveys. The neural networks are trained on data representing sailors, jobs, and constraints. The best performing models are multilayer perceptron and support vector machine with Adatron algorithms, which accurately classify jobs that experts decided should be offered to sailors.
The document proposes using neural networks to help an intelligent software agent called IDA learn to make high-quality decisions for assigning US Navy sailors to new jobs. Specifically, it evaluates using feedforward neural networks, multilayer perceptrons, and support vector machines to model the decision-making of human Navy personnel called detailers. The neural networks are trained on data from detailers involving sailors, jobs, and which jobs were offered to learn the constraints and preferences that guide human decisions. The goal is for the neural networks to help IDA's constraint satisfaction module make more human-like decisions over time as situations change.
Transfer learning allows machine learning models to recognize and apply knowledge learned from previous tasks or domains to new related tasks or domains. It involves identifying commonalities between tasks or domains and transferring knowledge, such as feature representations or model parameters. Common transfer learning approaches include instance-based methods that reweight source domain data, feature-based methods that learn shared feature representations, and parameter-based methods that learn shared parameters across related tasks. The goal is to leverage labeled data from source tasks or domains to improve learning on a related target task or domain with few or no labels.
Multi-core programming talk for weekly biostat seminarUSC
Gary K. Chen presented on using multi-core algorithms to speed up optimization. The seminar covered introduction to high-performance computing, concepts, an example of training a hidden Markov model, an example of regularized logistic regression, and closing remarks.
Fault-Tolerance Aware Multi Objective Scheduling Algorithm for Task Schedulin...csandit
Computational Grid (CG) creates a large heterogeneous and distributed paradigm to manage and execute the applications which are computationally intensive. In grid scheduling tasks are assigned to the proper processors in the grid system to for its execution by considering the execution policy and the optimization objectives. In this paper, makespan and the faulttolerance of the computational nodes of the grid which are the two important parameters for the task execution, are considered and tried to optimize it. As the grid scheduling is considered to be NP-Hard, so a meta-heuristics evolutionary based techniques are often used to find a solution for this. We have proposed a NSGA II for this purpose. The performance estimation ofthe proposed Fault tolerance Aware NSGA II (FTNSGA II) has been done by writing program in Matlab. The simulation results evaluates the performance of the all proposed algorithm and the results of proposed model is compared with existing model Min-Min and Max-Min algorithm which proves effectiveness of the model.
This document evaluates different multi-class classification methods using binary SVMs. It compares the 1-vs-1, 1-vs-many, and ECOC methods on various datasets from UCI and KDD. Experiments were run using SVMLight and results show accuracy and runtime for each method. The document outlines the objectives, implementation details, datasets used, and experimental results.
Final project, Machine Learning Having it Deep and Structured, NTU
- Rank 1/25 in peer review, original score: 16.2/17
- 2nd presentation pride (voted by audience)
The document discusses multi-layer perceptrons (MLPs) and the backpropagation algorithm. [1] MLPs can learn nonlinear decision boundaries using multiple layers of nodes and nonlinear activation functions. [2] The backpropagation algorithm is used to train MLPs by calculating error terms that are propagated backward to adjust weights throughout the network. [3] Backpropagation finds a local minimum of the error function through gradient descent and may get stuck but works well in practice.
The document proposes using neural networks to help an intelligent software agent called IDA learn to make high-quality decisions for assigning US Navy sailors to new jobs. Specifically, it evaluates using feedforward neural networks, multilayer perceptrons, and support vector machines to model the decision-making of human Navy personnel called detailers. The neural networks are trained on data from detailers involving sailors, jobs, and which jobs were offered to learn the constraints and preferences that guide human decisions. The goal is for the neural networks to help IDA's constraint satisfaction module make more human-like decisions over time as situations change.
Transfer learning allows machine learning models to recognize and apply knowledge learned from previous tasks or domains to new related tasks or domains. It involves identifying commonalities between tasks or domains and transferring knowledge, such as feature representations or model parameters. Common transfer learning approaches include instance-based methods that reweight source domain data, feature-based methods that learn shared feature representations, and parameter-based methods that learn shared parameters across related tasks. The goal is to leverage labeled data from source tasks or domains to improve learning on a related target task or domain with few or no labels.
Multi-core programming talk for weekly biostat seminarUSC
Gary K. Chen presented on using multi-core algorithms to speed up optimization. The seminar covered introduction to high-performance computing, concepts, an example of training a hidden Markov model, an example of regularized logistic regression, and closing remarks.
Fault-Tolerance Aware Multi Objective Scheduling Algorithm for Task Schedulin...csandit
Computational Grid (CG) creates a large heterogeneous and distributed paradigm to manage and execute the applications which are computationally intensive. In grid scheduling tasks are assigned to the proper processors in the grid system to for its execution by considering the execution policy and the optimization objectives. In this paper, makespan and the faulttolerance of the computational nodes of the grid which are the two important parameters for the task execution, are considered and tried to optimize it. As the grid scheduling is considered to be NP-Hard, so a meta-heuristics evolutionary based techniques are often used to find a solution for this. We have proposed a NSGA II for this purpose. The performance estimation ofthe proposed Fault tolerance Aware NSGA II (FTNSGA II) has been done by writing program in Matlab. The simulation results evaluates the performance of the all proposed algorithm and the results of proposed model is compared with existing model Min-Min and Max-Min algorithm which proves effectiveness of the model.
This document evaluates different multi-class classification methods using binary SVMs. It compares the 1-vs-1, 1-vs-many, and ECOC methods on various datasets from UCI and KDD. Experiments were run using SVMLight and results show accuracy and runtime for each method. The document outlines the objectives, implementation details, datasets used, and experimental results.
Final project, Machine Learning Having it Deep and Structured, NTU
- Rank 1/25 in peer review, original score: 16.2/17
- 2nd presentation pride (voted by audience)
The document discusses multi-layer perceptrons (MLPs) and the backpropagation algorithm. [1] MLPs can learn nonlinear decision boundaries using multiple layers of nodes and nonlinear activation functions. [2] The backpropagation algorithm is used to train MLPs by calculating error terms that are propagated backward to adjust weights throughout the network. [3] Backpropagation finds a local minimum of the error function through gradient descent and may get stuck but works well in practice.
The document discusses predicting the short-term and long-term memorability of videos using machine learning models. It extracts features like C3D, HMP, captions and InceptionV3 from videos and uses models like SVR, XGBoost and Bayesian Ridge Regression. It finds that combining features gives better memorability prediction scores than individual features. A neural network further improves scores over traditional models when trained on a combination of all features. Future work could explore other video features and deep learning models.
This presentation was first given at INFORMS in November 2013. It presents an analysis of the features that had the most impact on MIP solver performance during the last 12 years.
More presentations are available at https://www.ibm.com/developerworks/community/groups/community/DecisionOptimization
Presented for the first time at INFORMS in November 2013, this deck explains how CPLEX 12.5.1 exploits random performance variability through parallel root cut loops.
1) The document describes a project to automate the identification of individual fin whales from photographs by applying machine learning techniques. It involves segmenting whale images to isolate identifying features, extracting features from the images using pre-trained convolutional neural networks, and classifying the whales based on these features.
2) The dataset contained 884 images of 79 individual whales, which is much smaller than datasets used in previous whale identification research. This limited the complexity of models that could be trained without overfitting. Significant effort was spent preprocessing the images to improve the signal-to-noise ratio before classification.
3) Various techniques were tested for segmentation, including Markov random fields and hidden Markov random field expectation maximization. Features were then
This document discusses various solution methods for solving the unit commitment problem in power systems. It describes dynamic programming, Lagrangian relaxation, mixed integer programming, Benders decomposition, and heuristic methods. The key strengths and weaknesses of each method are outlined. The document concludes that a comprehensive algorithm combining the strengths of different methods while overcoming their individual weaknesses would be a suitable approach for solving real-world unit commitment problems.
Deep reinforcement learning from scratchJie-Han Chen
1. The document provides an overview of deep reinforcement learning and the Deep Q-Network algorithm. It defines the key concepts of Markov Decision Processes including states, actions, rewards, and policies.
2. The Deep Q-Network uses a deep neural network as a function approximator to estimate the optimal action-value function. It employs experience replay and a separate target network to stabilize learning.
3. Experiments applying DQN to the Atari 2600 game Space Invaders are discussed, comparing different loss functions and optimizers. The standard DQN configuration with MSE loss and RMSProp performed best.
A FUZZY MATHEMATICAL MODEL FOR PEFORMANCE TESTING IN CLOUD COMPUTING USING US...ijseajournal
Software product development life cycle has software testing as an integral part. Conventional testing
requires dedicated infrastructure and resources that are expensive and only used sporadically. In the
growing complexity of business applications it is harder to build in-house testing facilities and also to
maintain that mimic real-time environments. By nature, cloud computing provides resources which are
unlimited in nature along with flexibility, scalability and availability of distributed testing environment,
thus it has opened up new opportunities for software testing. It leads to cost-effective solutions by reducing
the execution time of large application testing. As a part of infrastructure resource, cloud testing can attain
its efficiency by taking care of the parameters like network traffic, Disk Storage and RAM speed. In this
paper we propose a new fuzzy mathematical model to attain better scope for the above parameters.
This document proposes new task prioritization rules for project execution that aim to improve upon the rules advocated by Critical Chain Project Management (CCPM). It presents the currently accepted CCPM rule for prioritizing tasks within a single project. Through computer simulations of over 970,000 possible cases, the proposed new rule that prioritizes based on the integration point of feeding chains is shown to result in significantly shorter project lead times. The document also proposes a new rule for prioritizing tasks across multiple projects to avoid mixing task priorities between projects. Another simulation compares this rule to the CCPM approach and again finds the proposed rule leads to faster project completion.
This document discusses CPU scheduling algorithms and proposes a new hybrid genetic algorithm approach. It provides an overview of common CPU scheduling algorithms like first-come first-served, round robin, shortest job first, and describes their characteristics. The document then proposes using a genetic algorithm that represents scheduling sequences as individuals to evolve better sequences through crossover and mutation until a minimum waiting time is reached. This new hybrid genetic algorithm is intended to provide more efficient CPU scheduling.
PR-134 How Does Batch Normalization Help Optimization?jaewon lee
1. The conventional view that batch normalization reduces internal covariate shift is tenuous and internal covariate shift is not a good predictor of training performance.
2. Batch normalization works by reparameterizing the model, which makes the optimization problem more stable and smooth by reducing loss variability and improving gradient predictiveness.
3. This smoothing effect encourages the training process to converge to flatter minima, which can improve generalization.
The document discusses a framework for characterizing web server workloads using machine learning techniques and generating synthetic workloads. It proposes using clustering algorithms to analyze trace data and characterize workloads into equivalence classes. These classes would then be used to generate a representative synthetic workload that could be scaled up for testing systems under various conditions. The document outlines the methodology, discusses related work, and provides initial results from clustering analysis on World Cup 1998 web trace data to demonstrate a proof of concept for the workload characterization approach.
305Vehicle Door Sag Evaluation Using FEAIJERD Editor
One of the many factors that lead to first impression on quality aspect of the car is door itself. Vehicle door is the first part that customer handle while buying the car. Thus the quality of the door needs to be updated considering the present and future handling of door. When the car is new the performance of door is almost satisfactory, but in some situations like unusual handling of door by kids, unusual handling during servicing or repairing some extra vertical force may get applied to the door. And that affect door working and results in to door sag.
Dokumen tersebut membahas tentang teori kinetik gas dan hukum-hukum yang terkait, termasuk hukum Boyle, hukum Charles, hukum Gay-Lussac, dan persamaan umum gas ideal.
This document describes a study that uses support vector machines for feature selection from 2D gas chromatography (GC×GC) images of Chinese herb samples. The study aims to improve classification and clustering of the herb data. It finds that selecting prominent patterns from the GC×GC images via linear SVM leads to better classification accuracy compared to using the full dataset, and improves clustering performance compared to using the raw 2D GC data, as shown by PCA analysis. Feature selection reduces computational complexity while retaining important distinguishing characteristics for analysis.
The document discusses predicting the short-term and long-term memorability of videos using machine learning models. It extracts features like C3D, HMP, captions and InceptionV3 from videos and uses models like SVR, XGBoost and Bayesian Ridge Regression. It finds that combining features gives better memorability prediction scores than individual features. A neural network further improves scores over traditional models when trained on a combination of all features. Future work could explore other video features and deep learning models.
This presentation was first given at INFORMS in November 2013. It presents an analysis of the features that had the most impact on MIP solver performance during the last 12 years.
More presentations are available at https://www.ibm.com/developerworks/community/groups/community/DecisionOptimization
Presented for the first time at INFORMS in November 2013, this deck explains how CPLEX 12.5.1 exploits random performance variability through parallel root cut loops.
1) The document describes a project to automate the identification of individual fin whales from photographs by applying machine learning techniques. It involves segmenting whale images to isolate identifying features, extracting features from the images using pre-trained convolutional neural networks, and classifying the whales based on these features.
2) The dataset contained 884 images of 79 individual whales, which is much smaller than datasets used in previous whale identification research. This limited the complexity of models that could be trained without overfitting. Significant effort was spent preprocessing the images to improve the signal-to-noise ratio before classification.
3) Various techniques were tested for segmentation, including Markov random fields and hidden Markov random field expectation maximization. Features were then
This document discusses various solution methods for solving the unit commitment problem in power systems. It describes dynamic programming, Lagrangian relaxation, mixed integer programming, Benders decomposition, and heuristic methods. The key strengths and weaknesses of each method are outlined. The document concludes that a comprehensive algorithm combining the strengths of different methods while overcoming their individual weaknesses would be a suitable approach for solving real-world unit commitment problems.
Deep reinforcement learning from scratchJie-Han Chen
1. The document provides an overview of deep reinforcement learning and the Deep Q-Network algorithm. It defines the key concepts of Markov Decision Processes including states, actions, rewards, and policies.
2. The Deep Q-Network uses a deep neural network as a function approximator to estimate the optimal action-value function. It employs experience replay and a separate target network to stabilize learning.
3. Experiments applying DQN to the Atari 2600 game Space Invaders are discussed, comparing different loss functions and optimizers. The standard DQN configuration with MSE loss and RMSProp performed best.
A FUZZY MATHEMATICAL MODEL FOR PEFORMANCE TESTING IN CLOUD COMPUTING USING US...ijseajournal
Software product development life cycle has software testing as an integral part. Conventional testing
requires dedicated infrastructure and resources that are expensive and only used sporadically. In the
growing complexity of business applications it is harder to build in-house testing facilities and also to
maintain that mimic real-time environments. By nature, cloud computing provides resources which are
unlimited in nature along with flexibility, scalability and availability of distributed testing environment,
thus it has opened up new opportunities for software testing. It leads to cost-effective solutions by reducing
the execution time of large application testing. As a part of infrastructure resource, cloud testing can attain
its efficiency by taking care of the parameters like network traffic, Disk Storage and RAM speed. In this
paper we propose a new fuzzy mathematical model to attain better scope for the above parameters.
This document proposes new task prioritization rules for project execution that aim to improve upon the rules advocated by Critical Chain Project Management (CCPM). It presents the currently accepted CCPM rule for prioritizing tasks within a single project. Through computer simulations of over 970,000 possible cases, the proposed new rule that prioritizes based on the integration point of feeding chains is shown to result in significantly shorter project lead times. The document also proposes a new rule for prioritizing tasks across multiple projects to avoid mixing task priorities between projects. Another simulation compares this rule to the CCPM approach and again finds the proposed rule leads to faster project completion.
This document discusses CPU scheduling algorithms and proposes a new hybrid genetic algorithm approach. It provides an overview of common CPU scheduling algorithms like first-come first-served, round robin, shortest job first, and describes their characteristics. The document then proposes using a genetic algorithm that represents scheduling sequences as individuals to evolve better sequences through crossover and mutation until a minimum waiting time is reached. This new hybrid genetic algorithm is intended to provide more efficient CPU scheduling.
PR-134 How Does Batch Normalization Help Optimization?jaewon lee
1. The conventional view that batch normalization reduces internal covariate shift is tenuous and internal covariate shift is not a good predictor of training performance.
2. Batch normalization works by reparameterizing the model, which makes the optimization problem more stable and smooth by reducing loss variability and improving gradient predictiveness.
3. This smoothing effect encourages the training process to converge to flatter minima, which can improve generalization.
The document discusses a framework for characterizing web server workloads using machine learning techniques and generating synthetic workloads. It proposes using clustering algorithms to analyze trace data and characterize workloads into equivalence classes. These classes would then be used to generate a representative synthetic workload that could be scaled up for testing systems under various conditions. The document outlines the methodology, discusses related work, and provides initial results from clustering analysis on World Cup 1998 web trace data to demonstrate a proof of concept for the workload characterization approach.
305Vehicle Door Sag Evaluation Using FEAIJERD Editor
One of the many factors that lead to first impression on quality aspect of the car is door itself. Vehicle door is the first part that customer handle while buying the car. Thus the quality of the door needs to be updated considering the present and future handling of door. When the car is new the performance of door is almost satisfactory, but in some situations like unusual handling of door by kids, unusual handling during servicing or repairing some extra vertical force may get applied to the door. And that affect door working and results in to door sag.
Dokumen tersebut membahas tentang teori kinetik gas dan hukum-hukum yang terkait, termasuk hukum Boyle, hukum Charles, hukum Gay-Lussac, dan persamaan umum gas ideal.
This document describes a study that uses support vector machines for feature selection from 2D gas chromatography (GC×GC) images of Chinese herb samples. The study aims to improve classification and clustering of the herb data. It finds that selecting prominent patterns from the GC×GC images via linear SVM leads to better classification accuracy compared to using the full dataset, and improves clustering performance compared to using the raw 2D GC data, as shown by PCA analysis. Feature selection reduces computational complexity while retaining important distinguishing characteristics for analysis.
The City of Berkeley is soliciting proposals for new self-check, materials security, and automated materials handling systems for all locations of the Berkeley Public Library. Proposals are due by March 11, 2010. The current systems from Checkpoint have proven problematic, with issues around media checkout and security tags. The library seeks a standards-based, integrated solution that maintains efficiencies while addressing current deficiencies. Vendors may propose RFID, barcode, or hybrid systems.
Statistical and Empirical Approaches to Spoken Dialog Systemsbutest
The document proposes a one-day workshop at AAAI-06 on statistical and empirical approaches for spoken dialog systems. The workshop will focus on machine learning techniques for dialog management and evaluation. It will include paper presentations and invited talks. The organizing committee includes researchers from universities in the US, UK, and Canada who have experience applying machine learning and statistical methods to dialog systems. The workshop aims to bring together researchers exploring how to represent and learn dialog models from data.
This document provides a bibliography of references related to information extraction and retrieval. It lists over 80 sources including journal articles, conference proceedings, book chapters, tutorials, and websites covering topics like text summarization, cross-language retrieval, information extraction techniques and systems, probabilistic models in IR, and using natural language processing for IR tasks. The references were compiled to provide background reading for a tutorial on information extraction and retrieval.
The document discusses Bayesian learning frameworks and genetic algorithm-based inductive learning (GABIL). It describes how GABIL uses a genetic algorithm and fitness function to learn disjunctive rules from examples, representing rules as bit strings. The system achieves classification accuracy comparable to decision tree learning methods. It also describes extensions like adding generalization operators that further improve performance.
This report summarizes Francisco Reinaldo's work in the first year of his PhD. It proposes a cognitive agent architecture called AFRANCI that is inspired by the human nervous system and includes different behavior levels and learning modules. The architecture will be empirically evaluated through agents in the RoboCup Rescue simulation. The report outlines Reinaldo's research activities and publications in the past year and outlines the plan to continue developing and testing the AFRANCI architecture in RoboCup Rescue over the next years of the PhD.
A study on dynamic load balancing in grid environmentIJSRD
Grid computing is a collection of computer resources from multiple locations to reach a common goal. Grid computing distinguishes from conventional high performance computing systems that are heterogeneous and geographically dispersed than cluster computer. One of the major issues in grid computing is load balancing. Classification of load balancing is: Static – Dynamic, Centralized – Decentralized, Homogeneous – Heterogeneous. Techniques like: Ant Colony Optimization, Threshold based and Optimal Heterogeneous are used by some researcher to balance the load. This survey paper discusses set of parameters to be used for comparing performance of each of them. In addition to that it says which technique is more useful for grid environment.
This document provides a practical guide for using support vector machines (SVMs) for classification tasks. It recommends beginners follow a simple procedure: 1) preprocess data by converting categorical features to numeric and scaling attributes, 2) use a radial basis function kernel, 3) perform cross-validation to select optimal values for hyperparameters C and γ, and 4) train the full model on the training set using the best hyperparameters. The guide explains why this procedure often provides reasonable results for novices and illustrates it using examples of real-world classification problems.
This document proposes a simple procedure for beginners to obtain reasonable results when using support vector machines (SVMs) for classification tasks. The procedure involves preprocessing data through scaling, using a radial basis function kernel, selecting model parameters through cross-validation grid search, and training the full model on the preprocessed data. The document provides examples applying this procedure to real-world datasets, demonstrating improved accuracy over approaches without careful preprocessing and parameter selection.
Cloud computing is a mix of distributed, grid and parallel processing. It is as of late in pattern on account of the
benefits it gives. It gives a pool of resources which are shared among different clients. Alongside its expanding request, it endures
with a few issues. A standout amongst the most vital and testing issue of cloud computing is load balancing. Load balancing
essentially intends to adjust the load similarly among a few hubs so hub is over-burden, under loaded or sitting inactive. Till date
there are numerous calculations proposed to deal with load balancing yet none of them has been demonstrated as productive one.
In this paper a load balancing algorithm is proposed utilizing rule of genetic algorithm. Fitness of assignments is ascertained and
on the premise of fitness load balancing is done. In this algorithm priority is appointed to the wellness computed in like manner
the chromosome with most noteworthy fitness is doled out least priority. Fitness here stands for the aggregate cost needs to
actualize an errand. Increasingly the cost more is the fitness. The entire simulation is performed on cloudsim 3.0 toolbox which is
JAVA based simulator.
What is Function approximation in RL and its types.pdfAiblogtech
As reinforcement learning (RL) allows agents to learn from their environment through interaction. It has attracted a lot of attention in the fields of artificial intelligence and machine learning. Function approximation is a key component of reinforcement learning (RL) that enables agents to generalize their knowledge and make wise judgements in scenarios not explicitly encountered in training. This study seeks to give readers a thorough grasp of function approximation in reinforcement learning (RL), its importance, and the numerous kinds used in diverse applications.
1. Introduction
Strengthening When an agent interacts with its surroundings, it learns the best ways to optimize cumulative rewards over a period of time. The state and action spaces in many real-world settings can be large and continuous, making it impractical to store and process all possible combinations computationally. This problem is addressed by function approximation, which enables agents to make defensible decisions without doing a thorough investigation by modelling and approximating the value or policy functions.
2. Basics of Reinforcement Learning
Prior to diving into function approximation, it is imperative to quickly go over the core ideas of reinforcement learning. In reinforcement learning, an agent looks at the state of the world as it is, acts, is rewarded, and changes to a new state. The agent wants to maximize the predicted cumulative reward by learning a policy that associates states with actions.
3. The Need for Function Approximation
Maintaining explicit representations of value functions or policies is impractical in many real-life learning situations. Due to the large and continuous state and action spaces. The curse of dimensionality poses a serious challenge, resulting in higher memory and processing demands. This problem is addressed by function approximation, which allows agents to make sensible judgements in unknown conditions by generalizing previously learned information.
4. Types of Function Approximation in RL
4.1 Linear Function Approximation
A straightforward yet effective method for approximating value or policy functions is the linear function approximation, which uses a linear combination of features. Training teaches the weights associated with these qualities, which indicate pertinent parts of the state. The linear function approximation, although straightforward, has proven effective in a variety of reinforcement learning situations, including as control and prediction tasks.
4.2 Polynomial Function Approximation
By adding higher-order elements, polynomial function approximation expands on the concept of linear approximation. As a result, nonlinear interactions between state characteristics and values can be captured by the model. The curse of dimensionality can make polynomial function approximation difficult in high-dimensional domains, even though it is more expressive than linear approximation.
Advancement in wireless communications lead more and more mobile wireless networks e.g.,
mobile networks [mobile ad hoc networks (MANETs)], wireless sensor networks, etc. Some of
the challenges in MANET include: Dynamic network topology, Speed, Bandwidth, computation
capability, Scalability, Quality of service, Secure and Reliable routing. One of the most
important challenges in mobile wireless networks is the Secure and reliable routing and the
main characteristic of MANET with respect to security is the lack of clear line of defence.
Therefore, the SP routing problem in MANET turns into dynamic optimization problem. In this
paper, a path detection algorithm and a model to detect intruders that is misbehaving nodes in
the alternative paths is proposed.
Cloud Partitioning of Load Balancing Using Round Robin ModelIJCERT
Abstract: The purpose of load balancing is to look up the performance of a cloud environment through an appropriate
circulation strategy. Good load balancing will construct cloud computing for more stability and efficiency. This paper
introduces a better round robin model for the public cloud based on the cloud partitioning concept with a switch mechanism
to choose different strategies for different situations. Load balancing is the process of giving out of workload among
different nodes or processor. It will introduces a enhanced approach for public cloud load distribution using screening and
game theory concept to increase the presentation of the system.
Hybrid of Ant Colony Optimization and Gravitational Emulation Based Load Bala...IRJET Journal
This document proposes a hybrid Ant Colony Optimization (ACO) and Gravitational Emulation Local Search (GELS) algorithm for load balancing in cloud computing. ACO is combined with GELS to take advantage of both algorithms - ACO is good for global search using pheromone trails while GELS is powerful for local search based on gravitational attraction. The hybrid algorithm is tested using CloudSim and shows improvements over existing algorithms like GA-GELS in metrics like resource utilization, makespan, and load balancing level.
A Framework for Performance Analysis of Computing Cloudsijsrd.com
This document presents a modified weighted active monitoring load balancing algorithm for cloud computing. It aims to improve performance over existing algorithms like Round Robin by taking into account both the load on each server and the current performance. The proposed algorithm calculates factors x (available resources), y (change in response time as a performance metric), and z (combining x and y) to generate a dynamic load balancing queue. It was implemented on a private cloud and showed improved performance over the Round Robin algorithm. Future work involves implementing the algorithm on a real-world cloud environment.
ieee standard base paper.-Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.
Support Vector Machine Optimal Kernel SelectionIRJET Journal
This document discusses selecting the optimal kernel for support vector machines (SVMs) based on different datasets. It provides background on SVMs and how their performance depends on the kernel function used. The document evaluates 4 kernel types (linear, polynomial, radial basis function (RBF), sigmoid) on 3 datasets: heart disease data, digit recognition data, and social network ads data. For each dataset and kernel combination, it reports accuracy, sensitivity, specificity, and kappa statistic metrics from implementing SVMs in R. The linear and RBF kernels generally performed best, with RBF working best for datasets with larger numbers of features like digit recognition data.
Partial half fine-tuning for object detection with unmanned aerial vehiclesIAESIJAI
Deep learning has shown outstanding performance in object detection tasks with unmanned aerial vehicles (UAVs), which involve the fine-tuning technique to improve performance by transferring features from pre-trained models to specific tasks. However, despite the immense popularity of fine-tuning, no works focused on to study of the precise fine-tuning effects of object detection tasks with UAVs. In this research, we conduct an experimental analysis of each existing fine-tuning strategy to answer which is the best procedure for transferring features with fine-tuning techniques. We also proposed a partial half fine-tuning strategy which we divided into two techniques: first half fine-tuning (First half F-T) and final half fine-tuning (Final half F-T). We use the VisDrone dataset for the training and validation process. Here we show that the partial half fine-tuning: Final half F-T can outperform other fine-tuning techniques and are also better than one of the state-of-the-art methods by a difference of 19.7% from the best results of previous studies.
Modified Active Monitoring Load Balancing with Cloud Computingijsrd.com
Cloud computing is internet-based computing in which large groups of remote servers are networked to allow the centralized data storage, and online access to computer services or resources. Load Balancing is essential for efficient operations in distributed environments. As Cloud Computing is growing rapidly and clients are demanding more services and better results, load balancing for the Cloud has become a very interesting and important research area. In the absence of proper load balancing strategy/technique the growth of CC will never go as per predictions. The main focus of this paper is to verify the approach that has been proposed in the model paper [3]. An efficient load balancing algorithm has the ability to reduce the data center processing time, overall response time and to cope with the dynamic changes of cloud computing environments. The traditional load balancing Active Monitoring algorithm has been modified to achieve better data center processing time and overall response time. The algorithm presented in this paper efficiently distributes the requests to all the VMs for their execution, considering the CPU utilization of all VMs.
Machine-learning scoring functions for molecular dockingPedro Ballester
Docking tools to predict whether and how a small molecule binds to a macromolecular target can be applied if a structural model of such target is available. The reliability of docking depends, however, on the accuracy of the adopted scoring function (SF). Despite intense research over the years, improving the accuracy of SFs for structure‐based binding affinity prediction or virtual screening has proven to be a challenging task for any class of method. New SFs based on modern machine‐learning regression models, which do not impose a predetermined functional form and thus are able to exploit effectively much larger amounts of experimental data, have recently been the object of much interest. These machine‐learning SFs have been shown to outperform a wide range of classical SFs at both binding affinity prediction and virtual screening. The emerging picture from these studies is that the classical approach of using linear regression with a small number of expert‐selected structural features can be strongly improved by a machine‐learning approach based on nonlinear regression allied with comprehensive data‐driven feature selection. As the performance of classical SFs does not grow with larger training datasets, this performance gap is expected to widen as more training data becomes available in the future.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document discusses query optimization techniques for efficient data retrieval from cloud storage. It proposes that query optimization is important from the customer's perspective to access needed data quickly. The paper outlines a three-step query processing approach involving parsing, optimization, and evaluation to generate an optimal plan for efficient query execution. An example transformation of an SQL query into a relational algebra query is provided to illustrate query optimization concepts for a distributed cloud storage system where relations are fragmented across multiple sites.
Types of Machine Learnig Algorithms(CART, ID3)Fatimakhan325
The document summarizes several machine learning algorithms used for data mining:
- Decision trees use nodes and edges to iteratively divide data into groups for classification or prediction.
- Naive Bayes classifiers use Bayes' theorem for text classification, spam filtering, and sentiment analysis due to their multi-class prediction abilities.
- K-nearest neighbors algorithms find the closest K data points to make predictions for classification or regression problems.
- ID3, CART, and k-means clustering are also summarized highlighting their uses, advantages, and disadvantages.
This document provides a practical guide for using support vector machines (SVMs) for classification tasks. It recommends beginners follow a simple procedure of transforming data, scaling it, using a radial basis function kernel, and performing cross-validation to select hyperparameters. Real-world examples show this procedure achieves better accuracy than approaches without these steps. The guide aims to help novices rapidly obtain acceptable SVM results without a deep understanding of the underlying theory.
Overlapped clustering approach for maximizing the service reliability ofIAEME Publication
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Optimizing Intelligent Agents Constraint Satisfaction with ...
1. Optimizing Intelligent Agent’s Constraint Satisfaction
with Neural Networks
Arpad Kelemen, Yulan Liang, Robert Kozma, Stan Franklin
Department of Mathematical Sciences
The University of Memphis
Memphis TN 38152
kelemena@msci.memphis.edu
Abstract. Finding suitable jobs for US Navy sailors from time to time is an
important and ever-changing process. An Intelligent Distribution Agent and
particularly its constraint satisfaction module take up the challenge to automate
the process. The constraint satisfaction module’s main task is to assign sailors
to new jobs in order to maximize Navy and sailor happiness. We present
various neural network techniques combined with several statistical criteria to
optimize the module’s performance and to make decisions in general. The data
was taken from Navy databases and from surveys of Navy experts. Such
indeterminate subjective component makes the optimization of the constraint
satisfaction a very sophisticated task. Single-Layer Perceptron with logistic
regression, Multilayer Perceptron with different structures and algorithms and
Support Vector Machine with Adatron algorithms are presented for achieving
best performance. Multilayer Perceptron neural network and Support Vector
Machine with Adatron algorithms produced highly accurate classification and
encouraging prediction.
1 Introduction
IDA, an Intelligent Distribution Agent [4], is a “conscious” [1], [2] software agent
being designed and implemented for the U.S. Navy by the Conscious Software
Research Group at the University of Memphis. IDA is intended to play the role of
Navy employees, called detailers, who assign sailors to new jobs from time to time.
To do so IDA is equipped with ten large modules, each of which is responsible for
one main task of the agent. One of them, the constraint satisfaction module, is
responsible for satisfying constraints in order to satisfy Navy policies, command
requirements, and sailor preferences. To better model humans IDA’s constraint
satisfaction is done through a behavior network [6] and “consciousness”, and employs
a standard linear functional approach to assign fitness values for each candidate job
for each candidate sailor one at a time. There is a function and a coefficient for each
of the soft-constraint, which are constraints that can be violated, and the job may still
be valid. Each of these functions measures how well the given constraint is satisfied
for the given sailor and the given job. Each of the coefficients measures how
important is the given constraint in relative to the others. There are hard constraints
2. too which can not be violated and they are implemented as Boolean multipliers for the
whole functional. A violation of a hard constraint yields 0 value for the functional.
The functional yields a value on [0,1] where higher values mean higher degree of
“match” between the given sailor and the given job at the given time. Tuning the
coefficients and the functions to improve the performance is a continuously changing,
though critical task. Using neural networks and statistical methods to enhance
decisions made by IDA’s constraint satisfaction module and to make better decisions
in general are the aim of this paper.
Using data from time to time coming from human detailers, a neural network may
learn to make human-like decisions for job assignments. On the other hand human
detailers, though have some preferences in judging jobs for sailors, can’t give specific
functions and numeric coefficients to be applied in a constraint satisfaction model.
Different detailers may have different views on the importance of constraints, which
view may largely depend on the sailor community they handle and may change from
time to time as the environment changes. However, setting up the coefficients in IDA
such a way that decisions reflect those made by humans is important. A neural
network may give us more insight in what preferences are important to a detailer and
how much. Moreover inevitable changes in the environment will result changes in
the detailer’s decisions, which could be learned with a neural network with some
delay.
In this paper, we present several approaches in order to achieve optimal decisions.
Feed-Forward Neural Network (FFNN) without and with one hidden layer for
training a logistic regression model were applied for searching for optimal
coefficients. For better generalization performance and model fitness we present the
Support Vector Machine (SVM) method. Sensitivity analysis through choosing
different network structures and algorithms were used to assess the stability of the
given approaches.
In Section 2 we describe how the data was attained and formulated into the input of
the neural networks. In Section 3 we discuss FFNNs with Logistic Regression Model
and the performance function and criteria of Neural Network Selection for best
performance including learning algorithm selection. After this we turn our interest to
Support Vector Machine to see if better performance can be achieved with it. Section
4 presents some comparison study and numerical results of all the presented
approaches along with the sensitivity analysis.
2 Preparing the Input for the Neural Networks
The data was extracted from the Navy’s Assignment Policy Management System’s
job and sailor databases. For the study one particular community, the Aviation
Support Equipment Technicians community was chosen. Note that this is the
community on which the current IDA prototype is being built. The databases
contained 467 sailors and 167 possible jobs for the given community. From the more
than 100 attributes in each database only those were selected which are important
from the viewpoint of the constraint satisfaction: 18 attributes from the sailor database
3. and 6 from the job database. The following four hard constraints were applied to
these attributes complying with Navy policies:
Table 1. The four hard constraints applied to the data set
Function Policy name Policy
c1 Sea Shore Rotation If a sailor's previous job was on shore then he
is only eligible for jobs at sea and vice versa
c2 Dependents Match If a sailor has more than 3 dependents then he
is not eligible for overseas jobs
c3 Navy Enlisted The sailor must have an NEC [trained skill]
Classification (NEC) what is required by the job
Match
c4 Paygrade Match (hard) The sailor’s paygrade can't be off by more
than one from the job's required paygrade
Note that the above definitions are simplified. Long, more accurate definitions
were applied on the data.
1277 matches passed the above four hard constraints, which were inserted into a
new database.
Four soft constraints were applied to the above matches, which were implemented
with functions. The function values after some preprocessing served as inputs to the
neural networks and were computed through knowledge given by Navy detailers.
Each of the function’s range is [0,1]. The functions were the following:
Table 2. The four soft constraints applied to the data set
Function Policy name Policy
f1 Job Priority Match The higher the job priority, the more
important to fill the job
f2 Sailor Location Preference It is better to send a sailor to a place he
Match wants to go
f3 Paygrade Match (soft) The sailor’s paygrade should exactly
match the job’s required paygrade
4. f4 Geographic Location Match Certain moves are more preferable than
others
Again, the above definitions are simplified. All the fi functions are monotone but
not necessarily linear. Note that monotony can be achieved in cases when we assign
values to set elements (such as location codes) by ordering.
Every sailor along with all his possible jobs satisfying the hard constraints were
assigned to a unique group. The numbers of jobs in each group were normalized into
[0,1] and were also included in the input to the neural network, which will be called
f5 in this paper. This is important because the output (decisions given by detailers)
were highly correlated: there was typically one job offered to each sailor.
Output data (decision) was acquired from an actual detailer in the form of Boolean
answers for each match (1 for jobs to be offered, 0 for the rest). Therefore we mainly
consider supervised learning methods.
3 Design of Neural Network
3.1 FFNN with Logistic Regression
One issue we are interested in is to see the relative importance of f 1-f4 and the
estimation of the conditional probability for the occurrence of the job to be offered.
This can be done through a logistic model.
In a logistic regression model the predicted class label, “decision” is generated
according to
y = P ( decision = 1 | w) = g ( wT f )
ˆ (3.1.1)
ea
g (a ) = (3.1.2)
1+ ea
Where g is a logistic function, a is the activation, w is a column vector of weights,
and f is a column vector of inputs “f1-f5“.
The weights in a logistic regression model (3.1.1) can be adapted using FFNN
topology [26]. In the simplest case it is only one input layer and one output logistic
layer. This is equivalent to the generalized linear regression model with logistic
function. The initial estimated weights were chosen from [0,1], so the linear
combination of weights with inputs f1-f4 will fall into [0,1]. It is a monotone function
of conditional probability, as shown in (3.1.1) and (3.1.2), so the conditional
probability of job to be offered can be monitored through the changing of the
5. combination of weights. The classification of decision can be achieved through the
C ( x) = arg max k pr ( x | y = k ) (3.1.3)
best threshold with the largest estimated conditional probability from group data. The
class prediction of an observation x from group y was determined by
The setup of the best threshold employed Receiver Operating Characteristic
(ROC), which provides the percentage of detections classified correctly and the
percentage of non-detections incorrectly classified giving different threshold as
presented in [27]. The range of the threshold in our case was [0,1].
To improve the generalization performance and achieve the best classification, the
FFNN with one hidden layer and one output layer was employed. Network
architectures with different degrees of complexity can be obtained through choosing
different types of sigmoid functions, number of hidden nodes and partition of data
which provides different size of examples for training, cross-validation and testing
sets.
3.2 Performance Function, Neural Network Selection and Criteria
The performance function commonly used in neural networks [11], [12], [13] is a
loss function plus a penalty term:
J = SSE + λ ∑ w 2 (3.2.1)
We propose an alternative function, which includes a penalty term as follows:
J = SSE + λn / N (3.2.2)
Where SSE is the Sum Square Error, λ is penalty factor, n is the number of
parameters in the network decided by the number of hidden nodes and N is the size of
the input examples’ set.
Through structural learning with minimizing the cost function (3.2.2), we would
like to find the best size of samples included in the network and also the best number
of hidden nodes. In our study the value of λ in (3.2.2) ranged from 0.01 to 1.0.
Normally the size of input samples should be chosen as large as possible in order to
keep the residual as small as possible. Due to the cost of a large size of input
examples, it may not be chosen as large as desired. In the other hand if the sample
size is fixed then the penalty factor combined with the number of hidden nodes should
be adjusted to minimize (3.2.2). For better generalization performance we need to
consider the size of testing and cross-validation sets. We designed a two-factorial
array to find the best partition of data into training, cross-validation and testing sets
with the number of hidden nodes given the value of λ.
Several criteria were applied in order to find the best FFNN:
1. Mean Square Error (MSE) defined as the Sum Square Error divided by the
degree of freedom
6. 2. The Correlation Coefficient (r) of the model shows the agreement between
the input and the output
3. The Akaike Information Criterion (AIC) defined as
AIC ( K a ) = −2 log( Lml ) + 2 K a (3.2.3)
4. Minimum Description Length (MDL) defined as
MDL( K a ) = − log( Lml ) + 0.5 K a log N (3.2.4)
Where Lml is the maximum likelihood of the model parameters and Ka is the
number of adjustable parameters. N is the size of the input examples’ set.
More epochs generally provide higher correlation coefficient and smaller MSE for
training. To avoid overfitting and to improve generalization performance training
was stopped when the MSE of the cross-validation set started to increase
significantly. Sensitivity analysis were performed through multiple test runs from
random starting points to decrease the chance of getting trapped in a local minimum
and to find stable results.
Note that the difference between AIC and MDL is that MDL includes the size of
the input examples which can guide us to choose appropriate partition of the data into
training, cross-validation and testing sets. The choice of the best network structure is
finally based on the maximization of predictive capability, which is defined as the
correct classification rate and the lowest cost given in (3.2.2).
3.3 Learning Algorithms for FFNN
Back propagation with momentum, conjugate gradient, quickprop and delta-delta
learning algorithms were applied for comparison study [10], [11]. The Back-
propagation with momentum algorithm has the major advantage of speed and is less
susceptible to trapping in local minima. Back-propagation adjusts the weights in the
steepest descent direction in which the performance function is decreasing most
rapidly but it does not necessarily produce the fastest convergence. The search of the
conjugate gradient is performed along conjugate directions, which produces generally
faster convergence than steepest descent directions. The Quickprop algorithm uses
information about the second order derivative of the performance surface to accelerate
the search. Delta-delta is an adaptive step-size procedure for searching a performance
surface.
The performance of best MLP with one hidden layer network obtained from above
was compared with popular classification method Support Vector Machine (SVM)
and Single-Layer Perceptron (SLP).
7. 3.4. Support Vector Machine
Support Vector Machine is a method for finding a hyperplane in a high
dimensional space that separates training samples of each class while maximizes the
minimum distance between the hyperplane and any training samples [20], [21], [22].
SVM approach can apply any kind of network architecture and optimization function.
We employed Radial Basis Function (RBF) network and Adatron algorithms. The
advantage of RBF is that it can place each data sample with Gaussian distribution so
as to transform the complex decision surface into a simpler surface and then use linear
N
J ( xi ) = λi (∑ λ j w j G ( xi − x j ,2σ 2 ) + b) (3.4.1)
j =1
discriminant functions. The learning algorithm employed Adatron algorithm [23],
[24], [25]. Adatron algorithm substitutes the inner product of patterns in the input
space by the kernel function of the RBF network. The performance function used as
presented in [27]:
Where λi is multiplier, wi is weight, G is Gaussian distribution and b is bias.
We chose a common starting multiplier (0.15), learning rate (0.70), and a small
M = min J ( xi ) (3.4.2)
i
threshold (0.01). While M is greater than the threshold, we choose a pattern x i to
perform the update. After update only some of the weights are different from zero
(called the support vectors), they correspond to the samples that are closest to the
boundary between classes. Adatron algorithm uses only those inputs for training that
are near the decision surface since they provide the most information about the
classification, so it provides good generalization and generally yields no overfitting
problems, so we do not need the cross-validation set to stop training early. The
Adatron algorithm in [27] can prune the RBF network so that its output for testing is
given by
g ( x) = sgn( ∑ λ w G( x − x ,2σ
i∈Support
i i i
2
) − b) (3.4.3)
Vectors
so it can adapt an RBF to have an optimal margin [25]. Various versions of RBF
networks (spread, error rate, etc.) were also applied but the results were far less
encouraging for generalization than with SVMs with the above method [10], [11].
4 Data Analysis and Results
FFNN with back-propagation with momentum without hidden layer gives the
weight estimation for the four coefficients: Job Priority Match, Sailor Location
Preference Match, Paygrade Match,
8. Geographic Location Match as follows: 0.316, 0.064, 0.358, 0.262 respectively.
Simultaneously we got the conditional probability for decisions of each observation
from (3.1.1). We chose the largest estimated logistic probability from each group as
predicted value for decisions equal to 1 (job to be offered) if it was over threshold.
The threshold was chosen to maximize performance and its value was 0.65. The
corresponding correct classification rate was 91.22% for the testing set.
Multilayer Perceptron (MLP) with one hidden layer was tested using tansig and
logsig activation functions for hidden and output layers respectively. Other activation
functions were also used but their performance was worse. Four different learning
algorithms were applied for comparison studies. For reliable results and to better
approximate the generalization performance for prediction each experiment was
repeated 10 times with 10 different initial weights. The reported values are averaged
over the 10 independent runs. Training was confined to 5000 epochs, but in most
cases there were no significant improvement in the MSE after 1000 epochs. The best
FFNNs were chosen by modifying the number of hidden nodes ranging from 2 to 20,
while the training set size was setup as 50%, 60%, 70%, 80% and 90% of the sample
set. The cross-validation and testing sets each took the half of the rest. We used 0.1
for the penalty factor λ, which has better generalization performance then other values
for our data set. Using MDL criteria we can find out the best match of percentage of
training with the number of hidden nodes in a factorial array. Table 3 reports
MDL/AIC values for given number of hidden nodes and given testing set sizes. As
shown in the table for 2, 5 and 7 nodes 5% for testing, 5% for cross validation, and
90% for training provides the lowest MDL. For 9 nodes the lowest MDL was found
for 10% testing, 10% cross validation, and 80% training set sizes. For 10-11 nodes
the best MDL was reported for 20%-20% cross-validation and testing and 60%
training set sizes. For 12-20 nodes the best size for testing set was 25%. There
appears to be a trend that by increasing the number of hidden nodes the size of the
training set should be increased to lower the MDL and the AIC. Table 4 provides the
correlation coefficients between inputs and outputs for best splitting the data with
given number of hidden nodes. 12–20 hidden nodes with 50% training set provides
higher values of the Correlation Coefficient than other cases. Fig. 1 gives the correct
classification rates given different numbers of hidden nodes assuming the best
splitting of data. The results were consistent with Tables 3 and 4. The best MLP
network we chose was 15 nodes in the hidden layer and 25% testing set size. The
best MLP with one hidden layer, SLP and the SVM were compared. Fig. 2 shows
how the size of the testing set affects the correct classification rate for three different
methods (MLP, SLP, SVM). The best MLP with one hidden layer gives highly
accurate classification. The SVM performed a little better, which is not surprising
because of its properties. Early stopping techniques were employed to avoid
overfitting and better generalization performance. Fig. 3 shows the MSE of the
training and the cross-validation data with the best MLP with 15 hidden nodes and
50% training set size. The MSE of the cross validation data started to significantly
increase after 700 epochs, therefore we use 700 for future models. Fig. 4 shows
performance comparison of back-propagation with momentum, conjugate gradient
descent, quickprop, and delta-bar-delta learning algorithms for MLP with different
number of hidden nodes and best cutting of the sample set. As it can be seen their
9. performance were relatively close for our data set, and delta-delta performed the best.
MLP with momentum also performed well around 15 hidden nodes.
MLP with 15 hidden nodes and 25% testing set size gave approximately 6% error
rate, which is a very good generalization performance for predicting jobs to be offered
for sailors. Some noise is naturally present when humans make decisions in a limited
time frame. An estimated 15% difference would occur in the decisions even if the
same data would be presented to the same detailer at a different time. Different
detailers are also likely to make different decisions even under the same
circumstances. Moreover environmental changes would also further bias decisions.
Conclusion
A large number of neural network and statistics criteria were applied to either
improve the performance of the current way IDA handles constraint satisfaction or to
come up with alternatives. IDA’s constraint satisfaction module, neural networks and
traditional statistics methods are complementary with one another. MLP with one
hidden layer with 15 hidden nodes and 25% as testing set using back-propagation
with momentum and delta-delta learning algorithms provided good generalization
performance for our data set. SVM with RBF network architecture and Adatron
algorithm gave even more encouraging prediction performance for decision
classification. Coefficients for the existing IDA constraint satisfaction module were
also obtained via SLP with logistic regression. However, it is important to keep in
mind that periodic update of the coefficients as well as new neural network trainings
using networks and algorithms we presented are necessary to comply with changing
Navy policies and other environmental challenges.
Acknowledgement
This work is supported in part by ONR grant (no. N00014-98-1-0332). The
authors thank the “Conscious” Software Research group for its contribution to this
paper.
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11. Table 3. Factorial array for guiding the best model selection with correlated group data: values
of MDL/AIC up to 1000 epochs
# hidden nodes Size of testing set
H 5% 10% 15% 20% 25%
2 -57.2/-58.3 -89.9/-96.3 -172.3/181.7 -159.6/-171.1 -245.3/-258.5
5 -12.7/-15.3 -59.5/-74.7 -116.4/-138.9 -96.5/-124.3 -188.0/-219.7
7 13.9/10.3 -48.9/-27.8 -93.5/-62.2 -62.3/-100.9 -161.0/-116.9
9 46.0/41.5 7.4/-21.6 -22.1/-62.2 -15.1/-64.4 -91.7/-148.1
10 53.7/48.6 -15.1/-64.4 63.4/19.0 10.3/-41.9 -64.9/-127.6
11 70.1/64.5 41.1/8.3 152.4/103.6 29.2/-30.8 -52.4/-121.2
12 85.6/79.5 60.5/24.6 39.9/-13.2 44.5/-21.0 -27.7/-102.7
13 99.7/93.1 73.4/34.5 66.0/8.4 80.8/9.9 -14.2/-95.4
14 120.4/113.3 90.8/49.0 86.6/24.6 101.4/25.1 20.8/-67.6
15 131.5/123.9 107.0/62.7 95.6/29.2 113.9/32.2 38.5/-58.4
17 166.6/158.0 138.0/87.4 182.4/107.2 149.4/56.9 62.2/-26.6
19 191.2/181.7 181.5/124.9 166.1/109.5 185.8/82.6 124.0/5.7
20 201.2/191.1 186.6/127.1 231.3/143.0 193.2/84.5 137.2/12.8
Table 4. Correlation Coefficients of inputs with outputs for given number of hidden nodes and
the corresponding (best) cutting percentages
Number of Hidden Nodes Correlation Coefficient
(Percentage of training)
2 0.7017 (90%)
5 0.7016 (90%)
7 0.7126 (90%)
9 0.7399 (80%)
10 0.7973 (60%)
11 0.8010 (60%)
12 0.8093 (50%)
13 0.8088 (50%)
14 0.8107 (50%)
15 0.8133 (50%)
17 0.8148 (50%)
19 0.8150 (50%)
20 0.8148 (50%)
12. Fig. 1. The dotted line shows the correct classification rates for MLP with one hidden layer
with different number of hidden nodes. The solid line shows results of SLP projected out for
comparison. Both lines assume the best splitting of data for each node as reported in Table 4
Fig. 2. Different sample set sizes give different correct classification rates. Dotted line: MLP
with one hidden layer and 15 hidden nodes. Dash-2dotted line: SLP. Solid line: SVM
13. Fig. 3. Typical MSE of training (dotted line) and cross-validation (solid line) with the best
MLP with one hidden layer (H=15, training set size=50%)
Fig. 4. Correct classification rates with different number of hidden nodes using 50% training
set size for four different algorithms. Dotted line: Back-propagation with Momentum. Dash-
dotted line: Conjugate Gradient Descent. Dash-2dotted line: Quickprop. Solid line: Delta-
Delta algorithm