In this lecture, you will learn two of the most popular methods for classifying data points into a finite set of categories. Both methods are based on representing a classifier via its decision boundary which is a hyperplane. The parameters of the hyperplane are learned from training data by minimizing a particular loss function.
Kernelization algorithms for graph and other structure modification problemsAnthony Perez
The document discusses kernelization algorithms for graph modification problems. It begins by introducing graph modification problems, which take as input a graph and property and output the minimum number of modifications to the graph to satisfy the property. It then discusses using parameterized complexity to more efficiently solve NP-hard graph modification problems. In particular, it covers the concept of kernels, which are polynomial-time algorithms that reduce an instance to an equivalent instance of size bounded by a function of the parameter. The document provides an overview of generic reduction rules and the concept of branches that can be applied to graph modification problems. It also introduces the specific problem of proper interval completion and known results about its parameterized complexity.
This document summarizes different approaches for structure learning in graph neural networks. It discusses three main classes of methods: 1) metric-based learning which learns a similarity matrix between nodes, 2) probabilistic models which learn the parameters of a distribution over graphs, and 3) direct optimization which directly optimizes the graph adjacency matrix. The document provides examples of methods within each class and notes challenges such as the simplicity of probabilistic models and computational difficulties of direct optimization.
This document discusses important issues in machine learning for data mining, including the bias-variance dilemma. It explains that the difference between the optimal regression and a learned model can be measured by looking at bias and variance. Bias measures the error between the expected output of the learned model and the optimal regression, while variance measures the error between the learned model's output and its expected output. There is a tradeoff between bias and variance - increasing one decreases the other. This is known as the bias-variance dilemma. Cross-validation and confusion matrices are also introduced as evaluation techniques.
This document summarizes and compares two popular Python libraries for graph neural networks - Spektral and PyTorch Geometric. It begins by providing an overview of the basic functionality and architecture of each library. It then discusses how each library handles data loading and mini-batching of graph data. The document reviews several common message passing layer types implemented in both libraries. It provides an example comparison of using each library for a node classification task on the Cora dataset. Finally, it discusses a graph classification comparison in PyTorch Geometric using different message passing and pooling layers on the IMDB-binary dataset.
This document introduces support vector machines (SVMs), including their use of hyperplanes to create classifiers with maximal marginal widths. It discusses how SVMs solve convex optimization problems to find the optimal hyperplane. Kernels are introduced to project data into higher dimensional spaces to allow for nonlinear classification. The document concludes by applying an SVM to a gender classification problem based on mobile app usage, using a custom kernel to account for apps and app categories.
This document summarizes a presentation on graph kernels in chemoinformatics. It discusses using graph kernels to measure similarity between molecular graphs to analyze large families of structural and numerical objects. Specific graph kernels discussed include the treelets kernel, which extracts small labeled subtrees from graphs, and kernels based on cyclic similarity, which analyze relevant cycles in molecules. The treelets kernel is shown to outperform other graph kernels and molecular descriptors in predicting boiling points of molecules.
Kernelization algorithms for graph and other structure modification problemsAnthony Perez
The document discusses kernelization algorithms for graph modification problems. It begins by introducing graph modification problems, which take as input a graph and property and output the minimum number of modifications to the graph to satisfy the property. It then discusses using parameterized complexity to more efficiently solve NP-hard graph modification problems. In particular, it covers the concept of kernels, which are polynomial-time algorithms that reduce an instance to an equivalent instance of size bounded by a function of the parameter. The document provides an overview of generic reduction rules and the concept of branches that can be applied to graph modification problems. It also introduces the specific problem of proper interval completion and known results about its parameterized complexity.
This document summarizes different approaches for structure learning in graph neural networks. It discusses three main classes of methods: 1) metric-based learning which learns a similarity matrix between nodes, 2) probabilistic models which learn the parameters of a distribution over graphs, and 3) direct optimization which directly optimizes the graph adjacency matrix. The document provides examples of methods within each class and notes challenges such as the simplicity of probabilistic models and computational difficulties of direct optimization.
This document discusses important issues in machine learning for data mining, including the bias-variance dilemma. It explains that the difference between the optimal regression and a learned model can be measured by looking at bias and variance. Bias measures the error between the expected output of the learned model and the optimal regression, while variance measures the error between the learned model's output and its expected output. There is a tradeoff between bias and variance - increasing one decreases the other. This is known as the bias-variance dilemma. Cross-validation and confusion matrices are also introduced as evaluation techniques.
This document summarizes and compares two popular Python libraries for graph neural networks - Spektral and PyTorch Geometric. It begins by providing an overview of the basic functionality and architecture of each library. It then discusses how each library handles data loading and mini-batching of graph data. The document reviews several common message passing layer types implemented in both libraries. It provides an example comparison of using each library for a node classification task on the Cora dataset. Finally, it discusses a graph classification comparison in PyTorch Geometric using different message passing and pooling layers on the IMDB-binary dataset.
This document introduces support vector machines (SVMs), including their use of hyperplanes to create classifiers with maximal marginal widths. It discusses how SVMs solve convex optimization problems to find the optimal hyperplane. Kernels are introduced to project data into higher dimensional spaces to allow for nonlinear classification. The document concludes by applying an SVM to a gender classification problem based on mobile app usage, using a custom kernel to account for apps and app categories.
This document summarizes a presentation on graph kernels in chemoinformatics. It discusses using graph kernels to measure similarity between molecular graphs to analyze large families of structural and numerical objects. Specific graph kernels discussed include the treelets kernel, which extracts small labeled subtrees from graphs, and kernels based on cyclic similarity, which analyze relevant cycles in molecules. The treelets kernel is shown to outperform other graph kernels and molecular descriptors in predicting boiling points of molecules.
This document introduces deep learning approaches for predicting spatio-temporal flows. It discusses how deep learning uses hierarchical layers to model complex nonlinear relationships in spatial and temporal data without assuming a data generation process. Examples are given of applying deep learning to predict traffic flows using loop detector data and to forecast stock price movements using limit order book imbalances. The document outlines the configuration of deep learning models for these tasks and evaluates their performance versus traditional statistical approaches.
(DL hacks輪読) Variational Inference with Rényi DivergenceMasahiro Suzuki
This document discusses variational inference with Rényi divergence. It summarizes variational autoencoders (VAEs), which are deep generative models that parametrize a variational approximation with a recognition network. VAEs define a generative model as a hierarchical latent variable model and approximate the intractable true posterior using variational inference. The document explores using Rényi divergence as an alternative to the evidence lower bound objective of VAEs, as it may provide tighter variational bounds.
Applied Machine Learning For Search Engine Relevance charlesmartin14
The document discusses machine learning techniques for search engine relevance and personalized recommendations. It describes using linear regression models to predict relevance scores and regularization methods like Tikhonov regularization to avoid overfitting. It also discusses using empirical Bayesian models like the Poisson gamma model to estimate relevance probabilities. Finally, it mentions using techniques like singular value decomposition and non-negative matrix factorization to find patterns in user behavior data and remove noise.
Efficient end-to-end learning for quantizable representationsNAVER Engineering
발표자: 정연우(서울대 박사과정)
발표일: 2018.7.
유사한 이미지 검색을 위해 neural network를 이용해 이미지의 embedding을 학습시킨다. 기존 연구에서는 검색 속도 증가를 위해 binary code의 hamming distance를 활용하지만 여전히 전체 데이터 셋을 검색해야 하며 정확도가 떨어지는 다는 단점이 있다. 이 논문에서는 sparse한 binary code를 학습하여 검색 정확도가 떨어지지 않으면서 검색 속도도 향상시키는 해쉬 테이블을 생성한다. 또한 mini-batch 상에서 optimal한 sparse binary code를 minimum cost flow problem을 통해 찾을 수 있음을 보였다. 우리의 방법은 Cifar-100과 ImageNet에서 precision@k, NMI에서 최고의 검색 정확도를 보였으며 각각 98× 와 478×의 검색 속도 증가가 있었다.
rit seminars-privacy assured outsourcing of image reconstruction services in ...thahirakabeer
This document proposes a privacy-assured outsourced image reconstruction service (OIRS) in the cloud. It addresses challenges of security, complexity, and efficiency when outsourcing image services. The OIRS architecture uses random linear transformations to encrypt data before sending to the cloud. This allows the cloud to efficiently solve the encrypted optimization problem and return an encrypted result without learning private image information. Experimental results show the OIRS approach brings over 3x computational savings compared to traditional methods, while still effectively reconstructing images from the encrypted data with good visual quality.
This document provides an introduction to time series analysis using R. It discusses key concepts like stationarity, unit roots, and integrated processes. It demonstrates how to check for stationarity in the SPY ETF price series and returns. Non-stationary price data can be made stationary by taking the first difference (returns). Unit root tests like the Augmented Dickey-Fuller test are used to formally test for a unit root. The document also shows how to access real-time market data using the IBrokers package in R.
1. Recurrent neural networks (RNNs) allow information to persist from previous time steps through hidden states and can process input sequences of variable lengths. Common RNN architectures include LSTMs and GRUs which address the vanishing gradient problem of traditional RNNs.
2. RNNs are commonly used for natural language processing tasks like machine translation, sentiment classification, and named entity recognition. They learn distributed word representations through techniques like word2vec, GloVe, and negative sampling.
3. Machine translation models use an encoder-decoder architecture with an RNN encoder and decoder. Beam search is commonly used to find high-probability translation sequences. Performance is evaluated using metrics like BLEU score.
C. Guyon, T. Bouwmans. E. Zahzah, “Foreground Detection via Robust Low Rank Matrix Decomposition including Spatio-Temporal Constraint”, International Workshop on Background Model Challenges, ACCV 2012, Daejon, Korea, November 2012.
A Dimension Abstraction Approach to Vectorization in MatlabaiQUANT
The document presents an approach to vectorizing Matlab source code while preserving correctness and improving efficiency. It describes representing variable dimensionalities, rules for determining when vectorization is valid, and techniques like transpose transformations and additive reduction to enable more vectorizations. Evaluation on image processing and linear algebra examples showed speedups of 1.56x to 4.6x from vectorizing loop-based code.
This document discusses structured prediction problems in machine learning for natural language processing tasks. It covers using linear classifiers like perceptrons and SVMs for structured outputs by factorizing feature representations. Sequence labeling tasks are used as a running example, with explanations of how to apply the Viterbi algorithm for inference and conditional random fields for learning. Dependency parsing is presented as a case study for structured prediction.
This document summarizes a semi-supervised regression method that combines graph Laplacian regularization with cluster ensemble methodology. It proposes using a weighted averaged co-association matrix from the cluster ensemble as the similarity matrix in graph Laplacian regularization. The method (SSR-LRCM) finds a low-rank approximation of the co-association matrix to efficiently solve the regression problem. Experimental results on synthetic and real-world datasets show SSR-LRCM achieves significantly better prediction accuracy than an alternative method, while also having lower computational costs for large datasets. Future work will explore using a hierarchical matrix approximation instead of low-rank.
(DL hacks輪読) How to Train Deep Variational Autoencoders and Probabilistic Lad...Masahiro Suzuki
This document discusses techniques for training deep variational autoencoders and probabilistic ladder networks. It proposes three advances: 1) Using an inference model similar to ladder networks with multiple stochastic layers, 2) Adding a warm-up period to keep units active early in training, and 3) Using batch normalization. These advances allow training models with up to five stochastic layers and achieve state-of-the-art log-likelihood results on benchmark datasets. The document explains variational autoencoders, probabilistic ladder networks, and how the proposed techniques parameterize the generative and inference models.
Linear Discrimination Centering on Support Vector Machinesbutest
Support vector machines learn hyperplanes that maximize the margin between two classes of data points. They introduce slack variables to handle non-linearly separable data, trying to maximize margins while minimizing errors. Popular SVMs use kernel functions to map data into higher dimensions, finding good linear separators in this space. SVMs find optimal hyperplanes by solving a convex optimization problem, but can be slow for large datasets. SVMs generally achieve high accuracy compared to other methods.
This document provides tips and tricks for deep learning including data augmentation techniques, batch normalization, training procedures like epochs and mini-batch gradient descent, loss functions like cross-entropy loss, and parameter tuning methods such as transfer learning, adaptive learning rates, dropout, and early stopping. It also discusses good practices like overfitting small batches and gradient checking.
An Importance Sampling Approach to Integrate Expert Knowledge When Learning B...NTNU
The introduction of expert knowledge when learning Bayesian Networks from data is known to be an excellent approach to boost the performance of automatic learning methods, specially when the data is scarce. Previous approaches for this problem based on Bayesian statistics introduce the expert knowledge modifying the prior probability distributions. In this study, we propose a new methodology based on Monte Carlo simulation which starts with non-informative priors and requires knowledge from the expert a posteriori, when the simulation ends. We also explore a new Importance Sampling method for Monte Carlo simulation and the definition of new non-informative priors for the structure of the network. All these approaches are experimentally validated with five standard Bayesian networks.
Read more:
http://link.springer.com/chapter/10.1007%2F978-3-642-14049-5_70
Predicting organic reaction outcomes with weisfeiler lehman networkKazuki Fujikawa
This document discusses neural message passing networks for modeling quantum chemistry. It defines message passing networks as having message functions that update node states based on neighboring node states, vertex update functions that update node states based to accumulated messages, and a readout function that produces an output for the full graph. It provides examples of specific message, update, and readout functions used in existing message passing models like interaction networks and molecular graph convolutions.
Big Data Analysis with Signal Processing on GraphsMohamed Seif
This document discusses signal processing on graphs and big data analysis using graph theory concepts. It begins with introducing fundamental graph theory terms like nodes, edges, and adjacency matrices. It then explains how to define graph signals and how signal processing concepts like shifting, filtering, and Fourier transforms can be generalized to graphs. In particular, it describes how the graph shift replaces time shifts, graph filters are polynomials of the graph shift matrix, and the graph Fourier transform uses the eigenvectors of the graph shift matrix as the basis. The document concludes by discussing how eigenvalues represent frequencies on graphs and how filters affect the frequency content of graph signals.
We start with motivation, few examples of uncertainties. Then we discretize elliptic PDE with uncertain coefficients, apply TT format for permeability, the stochastic operator and for the solution. We compare sparse multi-index set approach with full multi-index+TT.
Tensor Train format allows us to keep the whole multi-index set, without any multi-index set truncation.
This document provides a tutorial on support vector machines (SVM) for binary classification. It outlines the key concepts of SVM including linear separable and non-separable cases, soft margin classification, solving the SVM optimization problem, kernel methods for non-linear classification, commonly used kernel functions, and relationships between SVM and other methods like logistic regression. Example code for using SVM from the scikit-learn Python package is also provided.
This document provides an introduction to support vector machines (SVM). It discusses the history and key concepts of SVM, including how SVM finds the optimal separating hyperplane with maximum margin between classes to perform linear classification. It also describes how SVM can learn nonlinear decision boundaries using kernel tricks to implicitly map inputs to high-dimensional feature spaces. The document gives examples of commonly used kernel functions and outlines the steps to perform classification with SVM.
This document introduces deep learning approaches for predicting spatio-temporal flows. It discusses how deep learning uses hierarchical layers to model complex nonlinear relationships in spatial and temporal data without assuming a data generation process. Examples are given of applying deep learning to predict traffic flows using loop detector data and to forecast stock price movements using limit order book imbalances. The document outlines the configuration of deep learning models for these tasks and evaluates their performance versus traditional statistical approaches.
(DL hacks輪読) Variational Inference with Rényi DivergenceMasahiro Suzuki
This document discusses variational inference with Rényi divergence. It summarizes variational autoencoders (VAEs), which are deep generative models that parametrize a variational approximation with a recognition network. VAEs define a generative model as a hierarchical latent variable model and approximate the intractable true posterior using variational inference. The document explores using Rényi divergence as an alternative to the evidence lower bound objective of VAEs, as it may provide tighter variational bounds.
Applied Machine Learning For Search Engine Relevance charlesmartin14
The document discusses machine learning techniques for search engine relevance and personalized recommendations. It describes using linear regression models to predict relevance scores and regularization methods like Tikhonov regularization to avoid overfitting. It also discusses using empirical Bayesian models like the Poisson gamma model to estimate relevance probabilities. Finally, it mentions using techniques like singular value decomposition and non-negative matrix factorization to find patterns in user behavior data and remove noise.
Efficient end-to-end learning for quantizable representationsNAVER Engineering
발표자: 정연우(서울대 박사과정)
발표일: 2018.7.
유사한 이미지 검색을 위해 neural network를 이용해 이미지의 embedding을 학습시킨다. 기존 연구에서는 검색 속도 증가를 위해 binary code의 hamming distance를 활용하지만 여전히 전체 데이터 셋을 검색해야 하며 정확도가 떨어지는 다는 단점이 있다. 이 논문에서는 sparse한 binary code를 학습하여 검색 정확도가 떨어지지 않으면서 검색 속도도 향상시키는 해쉬 테이블을 생성한다. 또한 mini-batch 상에서 optimal한 sparse binary code를 minimum cost flow problem을 통해 찾을 수 있음을 보였다. 우리의 방법은 Cifar-100과 ImageNet에서 precision@k, NMI에서 최고의 검색 정확도를 보였으며 각각 98× 와 478×의 검색 속도 증가가 있었다.
rit seminars-privacy assured outsourcing of image reconstruction services in ...thahirakabeer
This document proposes a privacy-assured outsourced image reconstruction service (OIRS) in the cloud. It addresses challenges of security, complexity, and efficiency when outsourcing image services. The OIRS architecture uses random linear transformations to encrypt data before sending to the cloud. This allows the cloud to efficiently solve the encrypted optimization problem and return an encrypted result without learning private image information. Experimental results show the OIRS approach brings over 3x computational savings compared to traditional methods, while still effectively reconstructing images from the encrypted data with good visual quality.
This document provides an introduction to time series analysis using R. It discusses key concepts like stationarity, unit roots, and integrated processes. It demonstrates how to check for stationarity in the SPY ETF price series and returns. Non-stationary price data can be made stationary by taking the first difference (returns). Unit root tests like the Augmented Dickey-Fuller test are used to formally test for a unit root. The document also shows how to access real-time market data using the IBrokers package in R.
1. Recurrent neural networks (RNNs) allow information to persist from previous time steps through hidden states and can process input sequences of variable lengths. Common RNN architectures include LSTMs and GRUs which address the vanishing gradient problem of traditional RNNs.
2. RNNs are commonly used for natural language processing tasks like machine translation, sentiment classification, and named entity recognition. They learn distributed word representations through techniques like word2vec, GloVe, and negative sampling.
3. Machine translation models use an encoder-decoder architecture with an RNN encoder and decoder. Beam search is commonly used to find high-probability translation sequences. Performance is evaluated using metrics like BLEU score.
C. Guyon, T. Bouwmans. E. Zahzah, “Foreground Detection via Robust Low Rank Matrix Decomposition including Spatio-Temporal Constraint”, International Workshop on Background Model Challenges, ACCV 2012, Daejon, Korea, November 2012.
A Dimension Abstraction Approach to Vectorization in MatlabaiQUANT
The document presents an approach to vectorizing Matlab source code while preserving correctness and improving efficiency. It describes representing variable dimensionalities, rules for determining when vectorization is valid, and techniques like transpose transformations and additive reduction to enable more vectorizations. Evaluation on image processing and linear algebra examples showed speedups of 1.56x to 4.6x from vectorizing loop-based code.
This document discusses structured prediction problems in machine learning for natural language processing tasks. It covers using linear classifiers like perceptrons and SVMs for structured outputs by factorizing feature representations. Sequence labeling tasks are used as a running example, with explanations of how to apply the Viterbi algorithm for inference and conditional random fields for learning. Dependency parsing is presented as a case study for structured prediction.
This document summarizes a semi-supervised regression method that combines graph Laplacian regularization with cluster ensemble methodology. It proposes using a weighted averaged co-association matrix from the cluster ensemble as the similarity matrix in graph Laplacian regularization. The method (SSR-LRCM) finds a low-rank approximation of the co-association matrix to efficiently solve the regression problem. Experimental results on synthetic and real-world datasets show SSR-LRCM achieves significantly better prediction accuracy than an alternative method, while also having lower computational costs for large datasets. Future work will explore using a hierarchical matrix approximation instead of low-rank.
(DL hacks輪読) How to Train Deep Variational Autoencoders and Probabilistic Lad...Masahiro Suzuki
This document discusses techniques for training deep variational autoencoders and probabilistic ladder networks. It proposes three advances: 1) Using an inference model similar to ladder networks with multiple stochastic layers, 2) Adding a warm-up period to keep units active early in training, and 3) Using batch normalization. These advances allow training models with up to five stochastic layers and achieve state-of-the-art log-likelihood results on benchmark datasets. The document explains variational autoencoders, probabilistic ladder networks, and how the proposed techniques parameterize the generative and inference models.
Linear Discrimination Centering on Support Vector Machinesbutest
Support vector machines learn hyperplanes that maximize the margin between two classes of data points. They introduce slack variables to handle non-linearly separable data, trying to maximize margins while minimizing errors. Popular SVMs use kernel functions to map data into higher dimensions, finding good linear separators in this space. SVMs find optimal hyperplanes by solving a convex optimization problem, but can be slow for large datasets. SVMs generally achieve high accuracy compared to other methods.
This document provides tips and tricks for deep learning including data augmentation techniques, batch normalization, training procedures like epochs and mini-batch gradient descent, loss functions like cross-entropy loss, and parameter tuning methods such as transfer learning, adaptive learning rates, dropout, and early stopping. It also discusses good practices like overfitting small batches and gradient checking.
An Importance Sampling Approach to Integrate Expert Knowledge When Learning B...NTNU
The introduction of expert knowledge when learning Bayesian Networks from data is known to be an excellent approach to boost the performance of automatic learning methods, specially when the data is scarce. Previous approaches for this problem based on Bayesian statistics introduce the expert knowledge modifying the prior probability distributions. In this study, we propose a new methodology based on Monte Carlo simulation which starts with non-informative priors and requires knowledge from the expert a posteriori, when the simulation ends. We also explore a new Importance Sampling method for Monte Carlo simulation and the definition of new non-informative priors for the structure of the network. All these approaches are experimentally validated with five standard Bayesian networks.
Read more:
http://link.springer.com/chapter/10.1007%2F978-3-642-14049-5_70
Predicting organic reaction outcomes with weisfeiler lehman networkKazuki Fujikawa
This document discusses neural message passing networks for modeling quantum chemistry. It defines message passing networks as having message functions that update node states based on neighboring node states, vertex update functions that update node states based to accumulated messages, and a readout function that produces an output for the full graph. It provides examples of specific message, update, and readout functions used in existing message passing models like interaction networks and molecular graph convolutions.
Big Data Analysis with Signal Processing on GraphsMohamed Seif
This document discusses signal processing on graphs and big data analysis using graph theory concepts. It begins with introducing fundamental graph theory terms like nodes, edges, and adjacency matrices. It then explains how to define graph signals and how signal processing concepts like shifting, filtering, and Fourier transforms can be generalized to graphs. In particular, it describes how the graph shift replaces time shifts, graph filters are polynomials of the graph shift matrix, and the graph Fourier transform uses the eigenvectors of the graph shift matrix as the basis. The document concludes by discussing how eigenvalues represent frequencies on graphs and how filters affect the frequency content of graph signals.
We start with motivation, few examples of uncertainties. Then we discretize elliptic PDE with uncertain coefficients, apply TT format for permeability, the stochastic operator and for the solution. We compare sparse multi-index set approach with full multi-index+TT.
Tensor Train format allows us to keep the whole multi-index set, without any multi-index set truncation.
This document provides a tutorial on support vector machines (SVM) for binary classification. It outlines the key concepts of SVM including linear separable and non-separable cases, soft margin classification, solving the SVM optimization problem, kernel methods for non-linear classification, commonly used kernel functions, and relationships between SVM and other methods like logistic regression. Example code for using SVM from the scikit-learn Python package is also provided.
This document provides an introduction to support vector machines (SVM). It discusses the history and key concepts of SVM, including how SVM finds the optimal separating hyperplane with maximum margin between classes to perform linear classification. It also describes how SVM can learn nonlinear decision boundaries using kernel tricks to implicitly map inputs to high-dimensional feature spaces. The document gives examples of commonly used kernel functions and outlines the steps to perform classification with SVM.
This document provides an overview of linear classifiers and support vector machines (SVMs) for text classification. It explains that SVMs find the optimal separating hyperplane between classes by maximizing the margin between the hyperplane and the closest data points of each class. The document discusses how SVMs can be extended to non-linear classification through kernel methods and feature spaces. It also provides details on solving the SVM optimization problem and using SVMs for classification.
This document provides an overview of support vector machines (SVMs) presented by Eric Xing at CMU. It discusses how SVMs find the optimal decision boundary between two classes by maximizing the margin between them. It introduces the concepts of support vectors, which are the data points that define the decision boundary, and the kernel trick, which allows SVMs to implicitly perform computations in higher-dimensional feature spaces without explicitly computing the feature mappings.
This document provides an introduction to support vector machines (SVM). It discusses the history and development of SVM, including its introduction in 1992 and popularity due to success in handwritten digit recognition. The document then covers key concepts of SVM, including linear classifiers, maximum margin classification, soft margins, kernels, and nonlinear decision boundaries. Examples are provided to illustrate SVM classification and parameter selection.
The document provides a course calendar for a class on Bayesian estimation methods. It lists the dates and topics to be covered over 15 class periods from September to January. The topics progress from basic concepts like Bayes estimation and the Kalman filter, to more modern methods like particle filters, hidden Markov models, Bayesian decision theory, and applications of principal component analysis and independent component analysis. One class is noted as having no class.
Tong is a data scientist in Supstat Inc and also a master students of Data Mining. He has been an active R programmer and developer for 5 years. He is the author of the R package of XGBoost, one of the most popular and contest-winning tools on kaggle.com nowadays.
Agenda:
Introduction of Xgboost
Real World Application
Model Specification
Parameter Introduction
Advanced Features
Kaggle Winning Solution
This document provides a summary of supervised learning techniques including linear regression, logistic regression, support vector machines, naive Bayes classification, and decision trees. It defines key concepts such as hypothesis, loss functions, cost functions, and gradient descent. It also covers generative models like Gaussian discriminant analysis, and ensemble methods such as random forests and boosting. Finally, it discusses learning theory concepts such as the VC dimension, PAC learning, and generalization error bounds.
This document describes the solutions and questions for a midterm exam in 6.036: Spring 2018. It provides instructions for taking the exam such as writing your name on each page and coming to the front to ask questions. The exam consists of 6 multiple choice questions worth a total of 100 points. Question 1 involves linear classification and calculating margins. Question 2 asks about sources of error in machine learning models. Question 3 involves choosing appropriate representations and loss functions for different prediction problems. Question 4 introduces radial basis features for nonlinear classification. Question 5 discusses shortcut connections in neural networks.
Distributed Coordinate Descent for Logistic Regression with RegularizationИлья Трофимов
Logistic regression with L1 and L2 regularization is a widely used technique for solving
classication and class probability estimation problems. With the numbers of both featurescand examples growing rapidly in the fields like text mining and clickstream data analysis parallelization and the use of cluster architectures becomes important. We present a novel algorithm for tting regularized logistic regression in the distributed environment. The algorithm splits data between nodes by features, uses coordinate descent on each node and line search to merge results globally. Convergence proof is provided. A modications of the algorithm addresses slow node problem. We empirically compare our program with several state-of-the art approaches that rely on different algorithmic and data spitting methods. Experiments demonstrate that our approach is scalable and superior when training on large and sparse datasets.
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Machine Learning: Prospects and Applications
58 October 2015, Berlin, Germany
[AAAI2021] Combinatorial Pure Exploration with Full-bandit or Partial Linear ...Yuko Kuroki (黒木祐子)
The document describes a new model called combinatorial pure exploration with partial linear feedback (CPE-PL) for decision making problems with combinatorial actions and limited feedback. CPE-PL generalizes previous models by allowing for nonlinear rewards and more limited feedback through a transformation matrix. The document proposes the first static algorithm for CPE-PL that provides sample complexity guarantees and runs faster than existing approaches. It also introduces a two-phased adaptive algorithm for the special case of CPE-BL with full-bandit linear feedback and proves its sample complexity is optimal up to logarithmic factors.
Regularization is used in deep learning to reduce generalization error by modifying the learning algorithm. Common regularization techniques for deep neural networks include:
1) Parameter norm penalties like L2 and L1 regularization that penalize the weights of a network. This encourages simpler models that generalize better.
2) Early stopping which obtains the model parameters at the point of lowest validation error during training, rather than at the end of training.
3) Data augmentation which creates additional fake training data through techniques like translation to improve robustness.
This document provides an introduction to support vector machines (SVMs) for text classification. It discusses how SVMs find an optimal separating hyperplane that maximizes the margin between classes. SVMs can handle non-linear classification through the use of kernels, which map data into a higher-dimensional feature space. The document outlines the mathematical formulations of linear and soft-margin SVMs, explains how the kernel trick allows evaluating inner products implicitly in that feature space, and summarizes how SVMs are used for classification tasks.
This lecture is part of the course Machine Learning: Basic Principles delivered at Aalto University. This lecture presents the basic anatomy of a machine-learning problem. We start with discussing of how to transform raw data into features and labels. Then we detail different representations of predictor and classifier mappings, e.g., decision trees and neural networks. We also introduce the notion of a loss function and the associated empirical risk. Finally, we discuss how to learn good predictors (classifiers) vie empirical risk minimization.
Recursion and Problem Solving in Java.
Topics:
Definition and divide-and-conquer strategies
Simple recursive algorithms
Fibonacci numbers
Dicothomic search
X-Expansion
Proposed exercises
Recursive vs Iterative strategies
More complex examples of recursive algorithms
Knight’s Tour
Proposed exercises
Teaching material for the course of "Tecniche di Programmazione" at Politecnico di Torino in year 2012/2013. More information: http://bit.ly/tecn-progr
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...Kaxil Naik
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
A presentation that explain the Power BI Licensing
Linear Classifiers
1. aalto-logo-en-3
A Classification Problem
Logistic Regression
Support Vector Classification
Wrap Up
CS-E3210 Machine Learning: Basic Principles
Lecture 5: Classification I
slides by Alexander Jung, 2017
Department of Computer Science
Aalto University, School of Science
Autumn (Period I) 2017
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3. aalto-logo-en-3
A Classification Problem
Logistic Regression
Support Vector Classification
Wrap Up
Material
this lecture is inspired by
video lectures of Andrew Ng
https://www.youtube.com/watch?v=-la3q9d7AKQ
https://www.youtube.com/watch?v=7F-CuXdTQ5k
lecture notes
http://cs229.stanford.edu/notes/cs229-notes1.pdf
Ch. 2.2 of the tutorial “Kernel Methods in Computer Vision”
by Ch. Lampert https://pub.ist.ac.at/~chl/papers/
lampert-fnt2009.pdf
lecture notes http://www.robots.ox.ac.uk/~az/
lectures/ml/lect2.pdf
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4. aalto-logo-en-3
A Classification Problem
Logistic Regression
Support Vector Classification
Wrap Up
In A Nutshell
today we consider classification problems
consider data points z with features x and label y
want to learn classifier h(·) for predicting y based on h(x)
today we consider parametric classifiers h(w,b)
a classifier is represented by parameters w, b
we learn/find optimal parameters w, b using training data X
once we have learnt optimal parameter, we can discard data !
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5. aalto-logo-en-3
A Classification Problem
Logistic Regression
Support Vector Classification
Wrap Up
Outline
1 A Classification Problem
2 Logistic Regression
3 Support Vector Classification
4 Wrap Up
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6. aalto-logo-en-3
A Classification Problem
Logistic Regression
Support Vector Classification
Wrap Up
Ski Resort Marketing
you are working in the marketing agency of a ski resort
hard disk full of webcam snapshots (gigabytes of data)
want to group them into “winter” and ”summer” images
you have only a few hours for this task ...
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8. aalto-logo-en-3
A Classification Problem
Logistic Regression
Support Vector Classification
Wrap Up
Labeled Webcam Snapshots
create dataset X by randomly selecting N = 6 snapshots
manually categorise/label them (y(i) = 1 for summer)
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9. aalto-logo-en-3
A Classification Problem
Logistic Regression
Support Vector Classification
Wrap Up
Towards an ML Problem
we have few labeled snapshots in X
need an algorithm/method/software-app to automatically
label all snapshots as either “winter” or “summer”
each snapshot is several MByte large
computational/time constraints force us to use more compact
representation (features)
what are good features of a snapshot for classifying summer
vs. winter?
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10. aalto-logo-en-3
A Classification Problem
Logistic Regression
Support Vector Classification
Wrap Up
Redness, Greenness and Blueness
summer images are expected to be more colourful
winter images of Alps tend to contain much “white” (snow)
lets use redness xr , greenness xg and blueness xb
redness xr :=
j∈pixels
r[j] − (1/2)(g[j] + b[j])
greenness xg :=
j∈pixels
g[j] − (1/2)(r[j] + b[j])
blueness xb :=
j∈pixels
b[j] − (1/2)(r[j] + g[j])
r[j], g[j], b[j] denote red/green/blue intensity of pixel j
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11. aalto-logo-en-3
A Classification Problem
Logistic Regression
Support Vector Classification
Wrap Up
A Classification Problem
labeled dataset X = {(x(i), y(i))}N
i=1
feature vector x(i) = (x
(i)
r , x
(i)
g , x
(i)
b )T ∈ R3
label y(i) = 1 for summer and y(i) = 0 for winter
find a classifier h(·) : R3 → {0, 1} with y ≈ h(x)
which hypothesis space H and loss L(z, h(·)) should we use?
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12. aalto-logo-en-3
A Classification Problem
Logistic Regression
Support Vector Classification
Wrap Up
Linear Regression Classifier
lets first try to recycle ideas from linear regression
use H = {h(w)(x) = wT x, for w ∈ Rd } and squared error loss
two shortcomings of this approach:
classifier h(w)
(x) can be any real number, while y ∈ {0, 1}
squared error loss would penalize correct decisions
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13. aalto-logo-en-3
A Classification Problem
Logistic Regression
Support Vector Classification
Wrap Up
Outline
1 A Classification Problem
2 Logistic Regression
3 Support Vector Classification
4 Wrap Up
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14. aalto-logo-en-3
A Classification Problem
Logistic Regression
Support Vector Classification
Wrap Up
Taking Label Space Into Account
lets exploit that labels y take only values 0 or 1
use predictor h(·) with h(x) ∈ [0, 1]
one such choice is
h(w,b)
(x) = g(wT
x + b) with g(z) := 1/(1 + exp(−z))
g(z) known as logistic or sigmoid function
classifier is parametrized by weight w and offset b
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16. aalto-logo-en-3
A Classification Problem
Logistic Regression
Support Vector Classification
Wrap Up
A Probabilistic Interpretation
LogReg predicts y ∈{0, 1} by h(x)=g(w ·x+b)∈[0, 1]
lets model the label y and features x as random variables
features x are given/observed/measured
conditional probabilities P{y = 1|x} and P{y = 0|x}
estimate P{y = 1|x} by h(w,b)(x)
this yields the following relation
P{y|x} = h(w,b)
(x)y
(1 − h(w,b)
(x))(1−y)
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17. aalto-logo-en-3
A Classification Problem
Logistic Regression
Support Vector Classification
Wrap Up
Logistic Regression
max. likelihood max
w,b
P{y|x}=h(w,b)(x)y (1−h(w,b)(x))(1−y)
max. P{y|x} equivalent to min. logistic loss
L((x, y), h(w,b)
(·)) := − log P{y|x}
= −y log h(w,b)
(x)−(1−y) log(1−h(w,b)
(x))
choose w and b via empirical risk minimisation
min
w
E{h(w,b)
(·)|X} =
1
N
N
i=1
L((x(i)
, y(i)
), h(·))
=
1
N
N
i=1
−y(i)
log h(x(i)
)−(1−y(i)
) log(1−h(x(i)
))
=
1
N
N
i=1
−y(i)
log g(wT
x(i)
+ b)−(1−y(i)
) log(1−g(wT
x(i)
+ b))
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18. aalto-logo-en-3
A Classification Problem
Logistic Regression
Support Vector Classification
Wrap Up
ID Card of Logistic Regression
input/feature space X = Rd
label space Y = [0, 1]
loss function L((x, y), h(·)) = −y log h(x)−(1−y) log(1−h(x))
hypothesis space
H = {h(w,b)(x)=g(wT x+b), with w ∈ Rd , b ∈ R}
classify y = 1 if h(w,b)(x) ≥ 0.5 and y = 0 otherwise
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19. aalto-logo-en-3
A Classification Problem
Logistic Regression
Support Vector Classification
Wrap Up
Classifying with Logistic Regression
logistic regression problem
min
w,b
1
N
N
i=1
−y(i)
log g(wT
x(i)
+ b)−(1−y(i)
) log(1−g(wT
x(i)
+ b))
denote optimal point by w0 and b0
evaluate h(x) = g(wT
0 x + b0) for new data point
h(x) is an estimate for P(y = 1|x)
let us classify y = 1 if h(x) ≥ 1/2 and y = 0 else
partitions X in R1 ={x:h(x)≥1/2} and R0 ={x:h(x)<1/2}
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21. aalto-logo-en-3
A Classification Problem
Logistic Regression
Support Vector Classification
Wrap Up
Learning a Logistic Regression Model
logistic regression problem
min
w,b
1
N
N
i=1
−y(i)
log g(wT
x(i)
+ b)−(1−y(i)
) log(1−g(wT
x(i)
+ b))
in contrast to LinReg, no closed-form solution here
however, we can use GD !
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22. aalto-logo-en-3
A Classification Problem
Logistic Regression
Support Vector Classification
Wrap Up
A Learning Algorithm for Classification
input: labeled data set X, step-size or learning rate α
output: classifier h(w,b)(x) = g(wT x + b)
initalize: k := 0 and w0 := 0 and b0 := 0
until stopping criterion satisfied do
(w(k+1)
, b(k+1)
):=(w(k)
, b(k)
)−α w,bE{h(w(k)
,b(k)
)
|X}
k := k + 1
set w := w(k), b := b(k)
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23. aalto-logo-en-3
A Classification Problem
Logistic Regression
Support Vector Classification
Wrap Up
Outline
1 A Classification Problem
2 Logistic Regression
3 Support Vector Classification
4 Wrap Up
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24. aalto-logo-en-3
A Classification Problem
Logistic Regression
Support Vector Classification
Wrap Up
Binary Linear Classifiers
logistic regression delivers a linear classifier
linear classifier specified by normal vector w and offset b
let us from now on code the binary labels as +1 and −1
output of linear classifier ˆy = I(h(w,b)(x) > 0) with linear
predictor h(w,b)(x) = wT x + b
we can use different loss functions for learning w and b!
seemingly, squared error loss is not good for binary labels
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25. aalto-logo-en-3
A Classification Problem
Logistic Regression
Support Vector Classification
Wrap Up
Minimizing Error Probability
eventually, we aim at low error probability P{ˆy = y}
using 0/1-loss L((x, y), h(·)) = I(ˆy = y) we can approximate
P{ˆy = y} ≈ (1/N)
N
i=1
L((x(i)
, y(i)
), h(·))
the optimal classifier is then obtained by
min
h(·)∈H
N
i=1
L((x(i)
, y(i)
), h(·))
non-convex non-smooth optimization problem ! (there is a
work-around as we see in next lecture :-)
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30. aalto-logo-en-3
A Classification Problem
Logistic Regression
Support Vector Classification
Wrap Up
Learning Linear Classifier via Hinge Loss
linear classifier h(w,b)(x) = wT x + b
choose w and b by minimizing hinge loss
L((x, y), h(w,b)
) = max{0, 1−y · h(w,b)
(x)}
= max{0, 1 − y · (wT
x + b)}
learn optimal classifier via empirical risk minimization
min
w,b
E(h(w,b)
|X) :=
1
N
N
i=1
L((x(i)
, y(i)
), h(w,b)
(·))
=
1
N
N
i=1
max{0, 1 − y(i)
(wT
x(i)
+ b)}
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31. aalto-logo-en-3
A Classification Problem
Logistic Regression
Support Vector Classification
Wrap Up
SVC Maximizes Margin
we can rewrite hinge loss as
L((x, y), h(w,b)
) = max{0, 1 − y · (wT
x + b)}
= min
ξ≥0
ξ s.t. ξ ≥ 1 − y · (wT
x + b)
“margin
minimizing hing loss means maximizing margin
min
w,b
E(h(w,b)
|X) =
1
N
N
i=1
max{0, 1 − y(i)
(wT
x(i)
+ b)}
=
1
N
min
ξ(i)≥0
N
i=1
ξ(i)
s.t. ξ(i)
≥ 1 − y(i)
· (wT
x(i)
+ b)
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33. aalto-logo-en-3
A Classification Problem
Logistic Regression
Support Vector Classification
Wrap Up
ID Card of Support Vector Classifier
input/feature space X = Rd
label space Y = {−1, 1}
loss function L((x, y), h(·)) = max{0, 1 − y · h(w,b)(x)}
hypothesis space
H = {h(w,b)(x)=wT x+b, with w ∈ Rd , b ∈ R}
classify y = 1 if h(w,b)(x) ≥ 0 and y = −1 else
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34. aalto-logo-en-3
A Classification Problem
Logistic Regression
Support Vector Classification
Wrap Up
Outline
1 A Classification Problem
2 Logistic Regression
3 Support Vector Classification
4 Wrap Up
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35. aalto-logo-en-3
A Classification Problem
Logistic Regression
Support Vector Classification
Wrap Up
What We Learned Today
how to formulize a classification problem
different loss functions yield different classification methods
LogReg with logistic loss; amounts to maximum likelihood
SVC with hinge-loss and amounts to max. margin
LogReg and SVC are both parametric and linear classifiers
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36. aalto-logo-en-3
A Classification Problem
Logistic Regression
Support Vector Classification
Wrap Up
Logistic Regression at a Glance
uses hypothesis space of linear classifiers
uses a probabilistic interpretation of predictions
tailored to particular likelihood (Gaussian ??)
ERM amounts to SMOOTH convex problem
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37. aalto-logo-en-3
A Classification Problem
Logistic Regression
Support Vector Classification
Wrap Up
Support Vector Classifier (Machine) at a Glance
uses hypothesis space of linear classifiers
based on geometry (maximum margin between classes)
can be extended by using feature methods (kernel methods)
ERM amounts to NON-SMOOTH cvx opt problem
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A Classification Problem
Logistic Regression
Support Vector Classification
Wrap Up
What Happens Next?
next lecture on two further classification methods (decision
trees and naive Bayes)
read Sec. 9.2 - 9.2.3 of
https://web.stanford.edu/~hastie/Papers/ESLII.pdf
fill out post-lecture questionnaire in MyCourses (contributes
to grade!)
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