We here just show a simple way to run the LS-SVM - the LS-SVM toolbox has many more options.
You first need to create:
X - a Datamatrix with your data. The data vector (Subjects) along the rows (1st dimesion), and biomarkers (features) along the columns (2nd dimension).
Outcome - a vector with 1 or 0 for each data vector indicating whether that data vector belongs to either class 1 or class 2. (Class 1 could, e.g., be 'patient', and class 2 “healthy control”).
The LS-SVM is then calculated by typing:
[pp, alpha, b,gam,sig2,mode] = lssvm(X,Outcome);
The important output parameter is here 'model', which can be used to classify another datamatrix using the fitted LS-SVM model. Let us assume we have another datamatrix named X2 (the columns of this matrix should obviously contain the same biomarkers as X above), we can then classify this data by typing
pp = simlssvm(model,X2);
pp is a vector containing the predicted class for each data vector.
tutorial/how_to_run_least-square_support_vector_machine_in_matlab.txt · Last modified: 2013/06/06 17:03 by Simon-Shlomo Poil