'Multi class classification' diaporamas de présentation

SIMS 290-2: Applied Natural Language Processing

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I256: Applied Natural Language Processing

I256: Applied Natural Language Processing. Marti Hearst October 18, 2006 (Many slides originally by Barbara Rosario, modified here). Today. Algorithms for Classification Binary classification Perceptron Winnow Support Vector Machines (SVM) Kernel Methods Multi-Class classification

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Data Classification

Data Classification. Rong Jin. Classification Problems. Given input: Predict the output (class label) Binary classification: Multi-class classification: Learn a classification function: Regression:. Examples of Classification Problem. Text categorization:. Politics Sport.

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Classification and Linear Classifiers

In the Name of God. Machine Learning. Classification and Linear Classifiers. Mohammad Ali Keyvanrad. Thanks to: M . Soleymani (Sharif University of Technology ) R. Zemel (University of Toronto ) p. Smyth (University of California, Irvine). 1393-1394 (Spring). Outline.

428 views • 40 slides

Classification and Linear Classifiers

In the Name of God. Machine Learning. Classification and Linear Classifiers. Mohammad Ali Keyvanrad. Thanks to: M . Soleymani (Sharif University of Technology ) R. Zemel (University of Toronto ) p. Smyth (University of California, Irvine). Fall 1392. Outline. Classification

1.84k views • 38 slides

Classification and Linear Classifiers

In the Name of God. Machine Learning. Classification and Linear Classifiers. Mohammad Ali Keyvanrad. Thanks to: M . Soleymani (Sharif University of Technology ) R. Zemel (University of Toronto ) p. Smyth (University of California, Irvine). 1392-1393 (2). Outline. Classification

381 views • 38 slides

Multi-Categorical Output Tasks

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Support Vector Machines Classification with A Very Large-scale Taxonomy

Support Vector Machines Classification with A Very Large-scale Taxonomy. Advisor ? Dr. Hsu Presenter ? Chien-Shing Chen Author: Tie-Yan Liu, Yiming Yang, Hao Wan, Hua-Jun Zeng, Zheng Chen, and Wei-Ying Ma. SIGKDD , 2004. Outline. Motivation Objective Introduction

488 views • 36 slides

Hierarchical Shape Classification Using Bayesian Aggregation

Hierarchical Shape Classification Using Bayesian Aggregation. Zafer Barutcuoglu Princeton University Christopher DeCoro. Shape Matching. Given two shapes, quantify the difference between them Useful for search and retrieval, image processing, etc.

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Hierarchical Shape Classification Using Bayesian Aggregation

Hierarchical Shape Classification Using Bayesian Aggregation. Zafer Barutcuoglu Princeton University Christopher DeCoro. Shape Matching. Given two shapes, quantify the difference between them Useful for search and retrieval, image processing, etc.

206 views • 20 slides

Constructing Category Hierarchies for Visual Recognition

Constructing Category Hierarchies for Visual Recognition. Marcin Marszaklek and Cordelia Schmid. Introduction. Hierarchical classification scales well in the number of classes: O(n^2): one- vs -one O(n): one- vs -rest O(log(n): classification tree

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Multi-class Classification

Multi-class Classification. Mu-Chun Su. Case I. Each pattern class is separable from the other classes by a single hyperplane. M classes need M Perceptrons. A decision can not be made if (1) more than one perceptron have positive outputs; (2) none of the perceptron has positiveb output.

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