Support Vector Machines

Introduction

• Using support vector machines (SVMs) to build a spam classifier.

Files included in this exercise

• ex6.m - Octave/MATLAB script for the rst half of the exercise
• ex6data1.mat - Example Dataset 1
• ex6data2.mat - Example Dataset 2
• ex6data3.mat - Example Dataset 3
• svmTrain.m - SVM training function
• svmPredict.m - SVM prediction function
• plotData.m - Plot 2D data
• visualizeBoundaryLinear.m - Plot linear boundary
• visualizeBoundary.m - Plot non-linear boundary
• linearKernel.m - Linear kernel for SVM
• [?] gaussianKernel.m - Gaussian kernel for SVM
• [?] dataset3Params.m - Parameters to use for Dataset 3
• ex6 spam.m - Octave/MATLAB script for the second half of the exercise
• spamTrain.mat - Spam training set
• spamTest.mat - Spam test set
• emailSample1.txt - Sample email 1
• emailSample2.txt - Sample email 2
• spamSample1.txt - Sample spam 1
• spamSample2.txt - Sample spam 2
• vocab.txt - Vocabulary list
• getVocabList.m - Load vocabulary list
• porterStemmer.m - Stemming function
• readFile.m - Reads a file into a character string
• submit.m - Submission script that sends your solutions to our servers
• [?] processEmail.m - Email preprocessing
• [?] emailFeatures.m - Feature extraction from emails
• ? indicates files you will need to complete

IDE : Octave_4.4.0

ex6.zip