Chap4-Part0 - Linear Models for Classification Introduction...

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Linear Models for Classification: Introduction Sargur N. Srihari University at Buffalo, State University of New York USA
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Topics • Regression vs Classification • Linear Classification Models • Converting probabilistic regression output to classification output • Three classes of classification models Machine Learning Srihari 2
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Regression vs Classification • Regression: – assign input vector x to one or more continuous target variables t • Classification: – Assign input vector x to one of K discrete classes C k , k = 1, . . . ,K . • Ordinal Regression: – Discrete classes have an ordering 3 Srihari Machine Learning
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Linear Classification Models • Common scenario: classes considered disjoint • Input space divided into decision regions • Linear model decision surfaces are linear functions of input x D 1 dim. hyperplane within D dim. input space • Data sets whose classes separated exactly by linear decision surface – Linearly separable data 4 Srihari Machine Learning
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Chap4-Part0 - Linear Models for Classification Introduction...

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