====== Support Vector Machines ======

Ćwiczenia bazujące na materiałach Andrew Ng.\\
Przed zajęciami przejrzyj wykłady [[https://class.coursera.org/ml/lecture/preview|XII]] \\
{{:pl:dydaktyka:ml:ex6.pdf|Instructions}} in English.

Ćwiczenia do pobrania (files to download): {{:pl:dydaktyka:ml:svm.zip|SVM}}

===== Lista i opis plików =====
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