Support Vector Machines - The Auton Lab

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Linear Classifiers x α f y est denotes +1 denotes -1 f(x,w,b) = sign(w. x - b) How would you classify this data? Copyright © 2001, 2003, Andrew W. Moore Support Vector Machines: Slide 3 Linear Classifiers x α f y est denotes +1 denotes -1 f(x,w,b) = sign(w. x - b) How would you classify this data? Copyright © 2001, 2003, Andrew W. Moore Support Vector Machines: Slide 4 2
Linear Classifiers x α f y est denotes +1 denotes -1 f(x,w,b) = sign(w. x - b) How would you classify this data? Copyright © 2001, 2003, Andrew W. Moore Support Vector Machines: Slide 5 Linear Classifiers x α f y est denotes +1 denotes -1 f(x,w,b) = sign(w. x - b) Any of these would be fine.. ..but which is best? Copyright © 2001, 2003, Andrew W. Moore Support Vector Machines: Slide 6 3
Page 1: Support Vector Machines Note to oth
Page 5 and 6: Maximum Margin x α f y est denotes
Page 7 and 8: Computing the margin width “Predi
Page 9 and 10: Computing the margin width The line
Page 11 and 12: Learning the Maximum Margin Classif
Page 13 and 14: Learning the Maximum Margin Classif
Page 15 and 16: denotes +1 denotes -1 Uh-oh! This i
Page 17 and 18: Learning Maximum Margin with Noise
Page 19 and 20: An Equivalent QP Maximize R ∑ α
Page 21 and 22: Suppose we’re in 1-dimension Not
Page 23 and 24: Common SVM basis functions z k = (
Page 25 and 26: Quadratic Dot Products ⎛ ⎜ ⎜
Page 27 and 28: Higher Order Polynomials Polynomial
Page 29 and 30: Maximize QP with Quintic basis func
Page 31 and 32: VC-dimension of an SVM • Very ver
Page 33: What You Should Know • Linear SVM

Support Vector Machines - The Auton Lab

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