K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.
A classi er can predict the class labels of new data after the training.
Proportion of class labels for the training can be imbalanced in
real-world data sets, and imbalanced data makes the training
difficult for a classi er. This is the case for Real-Time Bidding
(RTB) framework in online advertisement, and there are several
ways to deal with the problem to improve the performance of the
classi er.
https://github.com/telecombcn-dl/dlmm-2017-dcu
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Gradient Boosted Regression Trees in scikit-learnDataRobot
Slides of the talk "Gradient Boosted Regression Trees in scikit-learn" by Peter Prettenhofer and Gilles Louppe held at PyData London 2014.
Abstract:
This talk describes Gradient Boosted Regression Trees (GBRT), a powerful statistical learning technique with applications in a variety of areas, ranging from web page ranking to environmental niche modeling. GBRT is a key ingredient of many winning solutions in data-mining competitions such as the Netflix Prize, the GE Flight Quest, or the Heritage Health Price.
I will give a brief introduction to the GBRT model and regression trees -- focusing on intuition rather than mathematical formulas. The majority of the talk will be dedicated to an in depth discussion how to apply GBRT in practice using scikit-learn. We will cover important topics such as regularization, model tuning and model interpretation that should significantly improve your score on Kaggle.
본 논문에서는 분배형 강화학습(Distributional Reinforcement Learning)에서 벨만 다이내믹스를 통해 확률 분포를 학습하는 문제를 고려합니다. 이전 연구들은 각 반환 분포의 유한 개의 통계량을 신경망을 통해 학습하는 방법을 사용해왔으나, 이 방법은 반환 분포의 함수적 형태에 제한을 받아 제한적인 표현력을 가지며, 미리 정의된 통계량을 유지하는 것이 어려웠습니다. 본 논문에서는 이러한 제한을 없애기 위해 최대 평균 거리(Maximum Mean Discrepancy, MMD)라는 가설 검정 기술을 활용해 반환 분포의 결정론적인(의사 난수를 사용한) 표본들을 학습하는 방법을 제안합니다. 이를 통해 반환 분포와 벨만 타겟 간의 모든 모멘트(순간값)를 암묵적으로 일치시킴으로써 분배형 벨만 연산자의 수렴성을 보장하며, 분포 근사에 대한 유한 샘플 분석을 제시합니다. 실험 결과, 본 논문에서 제안한 방법은 분배형 강화학습의 기본 모델보다 우수한 성능을 보이며, Atari 게임에서 분산형 에이전트를 사용하지 않는 경우에도 최고 성적을 기록합니다.
K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.
A classi er can predict the class labels of new data after the training.
Proportion of class labels for the training can be imbalanced in
real-world data sets, and imbalanced data makes the training
difficult for a classi er. This is the case for Real-Time Bidding
(RTB) framework in online advertisement, and there are several
ways to deal with the problem to improve the performance of the
classi er.
https://github.com/telecombcn-dl/dlmm-2017-dcu
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Gradient Boosted Regression Trees in scikit-learnDataRobot
Slides of the talk "Gradient Boosted Regression Trees in scikit-learn" by Peter Prettenhofer and Gilles Louppe held at PyData London 2014.
Abstract:
This talk describes Gradient Boosted Regression Trees (GBRT), a powerful statistical learning technique with applications in a variety of areas, ranging from web page ranking to environmental niche modeling. GBRT is a key ingredient of many winning solutions in data-mining competitions such as the Netflix Prize, the GE Flight Quest, or the Heritage Health Price.
I will give a brief introduction to the GBRT model and regression trees -- focusing on intuition rather than mathematical formulas. The majority of the talk will be dedicated to an in depth discussion how to apply GBRT in practice using scikit-learn. We will cover important topics such as regularization, model tuning and model interpretation that should significantly improve your score on Kaggle.
본 논문에서는 분배형 강화학습(Distributional Reinforcement Learning)에서 벨만 다이내믹스를 통해 확률 분포를 학습하는 문제를 고려합니다. 이전 연구들은 각 반환 분포의 유한 개의 통계량을 신경망을 통해 학습하는 방법을 사용해왔으나, 이 방법은 반환 분포의 함수적 형태에 제한을 받아 제한적인 표현력을 가지며, 미리 정의된 통계량을 유지하는 것이 어려웠습니다. 본 논문에서는 이러한 제한을 없애기 위해 최대 평균 거리(Maximum Mean Discrepancy, MMD)라는 가설 검정 기술을 활용해 반환 분포의 결정론적인(의사 난수를 사용한) 표본들을 학습하는 방법을 제안합니다. 이를 통해 반환 분포와 벨만 타겟 간의 모든 모멘트(순간값)를 암묵적으로 일치시킴으로써 분배형 벨만 연산자의 수렴성을 보장하며, 분포 근사에 대한 유한 샘플 분석을 제시합니다. 실험 결과, 본 논문에서 제안한 방법은 분배형 강화학습의 기본 모델보다 우수한 성능을 보이며, Atari 게임에서 분산형 에이전트를 사용하지 않는 경우에도 최고 성적을 기록합니다.
Similar to KNN.pdf k nearest neghnor classifier in machine (20)
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
4. Example for Classification - Whether tissue is good? (3,7)
x y Class
7 7 Bad
7 4 Bad
3 4 Good
1 4 Good
(7,7) |7-3| + |7-7| = 0 Bad
(7,4) |7-3| + |4-7| = 7 Bad
(3,4) |3-3| + |4-7| = 3 Good
(1,4) |1-3| +|4-7| = 5 Good
5.
6. Basic Idea
● k-NN classification rule is to assign to a test sample
the majority category label of its k nearest training
samples
● In practice, k is usually chosen to be odd, so as to
avoid ties
● The k = 1 rule is generally called the nearest-neighbor
classification rule
9. Key points
○ K-NN is a non-parametric algorithm, which means it does not make any assumption on
underlying data.
○ It is also called a lazy learner algorithm because it does not learn from the training set
immediately instead it stores the dataset and at the time of classification, it performs
an action on the dataset.
○ KNN algorithm at the training phase just stores the dataset and when it gets new data,
then it classifies that data into a category that is much similar to the new data.
