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Unformatted text preview: Lecture 25: Perceptrons II Prof. Julia Hockenmaier juliahmr@illinois.edu http://cs.illinois.edu/fa11/cs440 CS440/ECE448: Intro to ArtiFcial Intelligence Binary classifcation Input: x = (x 1. x d ) R d Output: return the class predicted by h w ( x ) h w ( x ): if f( x )= wx > 0 return y = 1, else return y = 0 2 CS440/ECE448: Intro AI x 1 x 2 + + + + + + + + + x x x x x x x x x x Decision boundary f( x ) = 0 Binary classifcation: training Input: {( x i, y i )} with (x 1. x d ) R d y i {+1, 1} Task: ind weights w = (w w 1. w d ) R d+1 that deFne f( x ) = wx 3 CS440/ECE448: Intro AI x 1 x 2 + + + + + + + + + x x x x x x x x x x Decision boundary f( x ) = 0 Perceptron algorithm Given training data {( x 1 , y 1 ),,( x j , y j ),,( x N , y N )} Start with initial weight vector w Online update: Update w for each ( x j , y j ) w i := w i + ! (y j h w ( x j ))x i j Batch update: Go through entire data set before updating w " w i = # j ( ! (y j h w ( x ))x...
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This note was uploaded on 10/13/2011 for the course CS 440 taught by Professor Levinson,s during the Spring '08 term at University of Illinois, Urbana Champaign.
 Spring '08
 Levinson,S

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