Feature Selection Package Documentation

Support vector machines (SVMs) are a set of supervised learning methods that build models that predict whether a new data point fit into one of two categories. This is used for purposes of regression and classification of various features.

Method Signature:

[acc, predict_label] = svmPrediction (trainX, trainY , testX, testY, ker)

Output:
   acc: The accuracy of the classifier.
   predict_label: The label of the column that will give the most accurate predictions.

Input:
  trainX: training data, each row is an instance.
  trainY: training data, each column is a class.
  testX: testing data, each row is an instance.
  testY: testing data, each column is a class.
  ker: A structure, with the following fields:

ker -- 1 for linear, 2 for Radial-Basis Function (RBF)
gamma - can only be used with RBF
C

% Using the wine.dat data set, which can be found at
% [fspackage_location]/classifiers/knn/wine.mat
param.ker = 2;
param.gamma = 0;
svmPrediction(X,Y,X,Y,param)

BibTex entry for:

Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines, 2001.

@Manual{CC01a,
   author = {Chih-Chung Chang and Chih-Jen Lin},
   title = {{LIBSVM}: a library for support vector machines},
   year = {2001},
   note = {Software available at \url{http://www.csie.ntu.edu.tw/~cjlin/libsvm}
}