Support Vector Machines

Ćwiczenia bazujące na materiałach Andrew Ng.
Przed zajęciami przejrzyj wykłady XII
Instructions in English.

Ćwiczenia do pobrania (files to download): SVM

Pliki oznaczone znakiem wykrzyknika () należy wypełnić własnym kodem

• ex6.m - Octave script for the first 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 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
• submitWeb.m - Alternative submission script
• processEmail.m - Email preprocessing
• emailFeatures.m - Feature extraction from emails