Using Libsvm for learning -

a) In order to run algorithms using libsvm the data and codes need to be located within the appropriate folder. For e.g., in our case everything goes under /libsvm-3.12/matlab/ as we're using Octave.

b) The functions I used are - libsvmwrite, libsvmread, svmtrain, and svmpredict.

c) libsvmwrite/libsvmread - all .csv and .txt files need to be converted to libsvm format. This involves converting X into a sparse vector as required by the tool, writing it out in the correct format, and reading it back. Ref - lines
31 - 34 in the code.

d) svmtrain - used twice in the code, initially to choose the best C and gamma value, and then finally to train the
model. For cross-validation the option to specify is '-v N', where N is the number of buckets, and the output of svmtrain is the accuracy which is a scalar. Lines 58 - 75 contain this part.

e) Without -v, the output of svmtrain is a non-scalar model that can be directly plugged into svmpredict. Some basic parameters are kernel type, cost, gamma, and degree of cross validation.

f) Additional details at http://www.csie.ntu.edu.tw/~cjlin/libsvm/.