# lec3-svm - Machine Learning Lecture 3 Yang Yang Department...

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Machine Learning Lecture 3 Yang Yang Department of Computer Science & Engineering Shanghai Jiao Tong University

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Support Vector Machine
Linear classifiers–which line is better?

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Pick the one with the largest margin!
A Good Separator X X O O O O O O X X X X X X O O

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Noise in the Observations X X O O O O O O X X X X X X O O
Ruling Out Some Separators X X O O O O O O X X X X X X O O

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Lots of Noise X X O O O O O O X X X X X X O O
Maximizing the Margin X X O O O O O O X X X X X X O O

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Parameterizing the decision boundary m features
Parameterizing the decision boundary “confidence” for j th data point

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Distance between examples closest to the line/hyperplane margin = 2 ࠵? = 2a/ ǁ w ǁ Note: ‘a’ is arbitrary (can normalize equations by a) Maximizing the margin
Support Vector Machine Solve efficiently by quadratic programming (QP) well-studied solution algorithms

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Support Vector Machine Linear hyperplane defined by “support vectors” i: ( w . x i + b ) y i = 1 Moving other points a little doesn’t effect the decision boundary Only need to store the support vectors to predict labels of new points How many support vectors in linearly separable case? m+1
What if data is not linearly separable?

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What if data is still not linearly separable? Allow “error” in classification Maximize margin and minimize # mistakes on training data C - tradeoff parameter Not convex 0/1 loss (doesn’t distinguish between near miss and bad mistake)
What if data is still not linearly separable? Allow “error” in classification ξ j - “slack” variables (>1 if x j misclassifed) pay linear penalty if mistake C - tradeoff parameter(chosen by cross- validation) convex! Soft margin approach

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Soft-margin SVM Soften the constraints: ( w . x j + b ) y j 1- ξ j j ξ j 0 j Penalty for misclassifying: C ξ j How do we recover hard margin SVM? Set C =
Slack variables as Hinge loss Regularized loss Hinge loss

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Hinge Loss regularization Loss: hinge loss
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