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Linear Classifiers (LC) J.-S. Roger Jang ( 張智星 ) MIR Lab, CSIE Dept. National Taiwan University.

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Presentation on theme: "Linear Classifiers (LC) J.-S. Roger Jang ( 張智星 ) MIR Lab, CSIE Dept. National Taiwan University."— Presentation transcript:

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2 Linear Classifiers (LC) J.-S. Roger Jang ( 張智星 ) jang@mirlab.org http://mirlab.org/jang MIR Lab, CSIE Dept. National Taiwan University

3 2/6 Linear classifiers (LC) The output is based on linear combination of features Types Linear perceptrons SVM (support vector machine) Logistic regression … Introduction to Linear Classifiers

4 3/6 Goal Determine a person’s gender from his/her profile data Features collected Birthday Blood type Height and weight Density Three measures Hair length Voice pitch … Chromosome Example: Gender Classification Training data x 1 (hair length) x 2 (voice freq.)

5 4/6 Proposed by Widrow & Hoff in 1960 AKA ADALINE (Adaptive Linear Neuron) or single-layer perceptron Perceptrons Training data x 1 (hair length) x 2 (voice freq.) x1x1 x2x2 w1w1 w2w2 w0w0 y Quiz!

6 5/6 Characteristics of LC Guaranteed to converge to a set of weights that will perfectly classify all the data if such a solution exists Data rescaling is necessary to speed up convergence of the algorithm Stops whenever a solution with zero error rate is found Nonlinear decision boundaries can also be found by the adaptive technique For a k-class problem, it needs k(k-1)/2 decision boundaries to do complete classification Characteristics of LC

7 6/6 Demo of Perceptrons perceptronDemo.mlincTrain.m

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