'Perceptron algorithm' diaporamas de présentation

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Neural Networks

Neural Networks. L. Manevitz Dept of Computer Science manevitz@cs.haifa.ac.il Tel: 2420. Plan. Requirements Background Brains and Computers Computational Models Learnability vs Programming Representability vs Training Rules Abstractions of Neurons Abstractions of Networks

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The Problem: Consider a two class task with ? 1 , ? 2

LINEAR CLASSIFIERS. The Problem: Consider a two class task with ? 1 , ? 2. Hence:. The Perceptron Algorithm Assume linearly separable classes, i.e., The case falls under the above formulation, since. Our goal: Compute a solution, i.e., a hyperplane w , so that The steps

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Online Algorithms

Online Algorithms. Lecturer: Yishay Mansour Elad Walach Alex Roitenberg. Introduction. Up until now, our algorithms start with input and work with it suppose input arrives a little at a time, need instant response. Oranges example.

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Content

Content. Linear Learning Machines and SVM The Perceptron Algorithm revisited Functional and Geometric Margin Novikoff theorem Dual Representation Learning in the Feature Space Kernel-Induced Feature Space Making Kernels The Generalization Problem

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Content

Content. Linear Learning Machines and SVM The Perceptron Algorithm revisited Functional and Geometric Margin Novikoff theorem Dual Representation Learning in the Feature Space Kernel-Induced Feature Space Making Kernels The Generalization Problem

608 views • 47 slides

The Problem: Consider a two class task with ? 1 , ? 2

LINEAR CLASSIFIERS. The Problem: Consider a two class task with ? 1 , ? 2. Hence:. The Perceptron Algorithm Assume linearly separable classes, i.e., The case falls under the above formulation, since. Our goal: Compute a solution, i.e., a hyperplane w , so that The steps

561 views • 47 slides

The Problem: Consider a two class task with ? 1 , ? 2

LINEAR CLASSIFIERS. The Problem: Consider a two class task with ? 1 , ? 2. Hence:. The Perceptron Algorithm Assume linearly separable classes, i.e., The case falls under the above formulation, since. Our goal: Compute a solution, i.e., a hyperplane w , so that The steps

554 views • 47 slides

The Problem: Consider a two class task with ? 1 , ? 2

LINEAR CLASSIFIERS. The Problem: Consider a two class task with ? 1 , ? 2. Hence:. The Perceptron Algorithm Assume linearly separable classes, i.e., The case falls under the above formulation, since. Our goal: Compute a solution, i.e., a hyperplane w , so that The steps

547 views • 47 slides

The Problem: Consider a two class task with ? 1 , ? 2

LINEAR CLASSIFIERS. The Problem: Consider a two class task with ? 1 , ? 2. Hence:. The Perceptron Algorithm Assume linearly separable classes, i.e., The case falls under the above formulation, since. Our goal: Compute a solution, i.e., a hyperplane w , so that The steps

474 views • 47 slides

The Problem: Consider a two class task with ? 1 , ? 2

LINEAR CLASSIFIERS. The Problem: Consider a two class task with ? 1 , ? 2. Hence:. The Perceptron Algorithm Assume linearly separable classes, i.e., The case falls under the above formulation, since. Our goal: Compute a solution, i.e., a hyperplane w , so that The steps

474 views • 47 slides

Artificial Neural Networks

Artificial Neural Networks. Outline. Biological Motivation Perceptron Gradient Descent Least Mean Square Error Multi-layer networks Sigmoid node Backpropagation. Biological Neural Systems. Neuron switching time : > 10 -3 secs Number of neurons in the human brain: ~10 10

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The value of kernel function represents the inner product of two training points in feature space

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An Empirical Study on Language Model Adaptation

An Empirical Study on Language Model Adaptation. Jianfeng Gao , Hisami Suzuki, Microsoft Research Wei Yuan Shanghai Jiao Tong University. Presented by Patty Liu. Outline. Introduction The Language Model and the Task of IME Related Work LM Adaptation Methods Experimental Results

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An Empirical Study on Language Model Adaptation

An Empirical Study on Language Model Adaptation. Jianfeng Gao , Hisami Suzuki, Microsoft Research Wei Yuan Shanghai Jiao Tong University. Presented by Patty Liu. Outline. Introduction The Language Model and the Task of IME Related Work LM Adaptation Methods Experimental Results

528 views • 32 slides

HPSG parser development at U- tokyo

HPSG parser development at U- tokyo. Takuya Matsuzaki. University of Tokyo. Topics. Overview of U-Tokyo HPSG parsing system Supertagging with Enju HPSG grammar. Overview of U-Tokyo parsing system. Two different algorithms: E nju parser: Supertagging + CKY algo . for TFS

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METU Informatics Institute Min720 Pattern Classification with Bio-Medical Applications

METU Informatics Institute Min720 Pattern Classification with Bio-Medical Applications. Part 7 : Linear and G eneralized D iscriminant F unctions. LINEAR DISCRIMINANT FUNCTIONS. Assume that t he discriminant functions are linear function s of X.

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CS 480/680 : Intro to ML

CS 480/680 : Intro to ML. Lecture 01: Perceptron. Outline. Announcements Perceptron Extensions Next. Outline. Announcements Perceptron Extensions Next. Announcements. Assignment 1 unleashed tonight Projects NIPS: https://papers.nips.cc/ ICML: http://proceedings.mlr.press/v80/

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Max-margin sequential learning methods

Max-margin sequential learning methods. William W. Cohen CALD. Announcements. Upcoming assignments: Wed 3/3: project proposal due: personnel + 1-2 page Spring break next week, no class Will get feedback on project proposals by end of break

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