SVMlight is an implementation of Vapnik's Support Vector Machine for the problem of pattern recognition, for the problem of regression, and for the problem of learning a ranking function. More details at SVM website.
General options:
-? - this help
-v [0..3] - verbosity level (default 1) Learning options:
-z {c,r,p} - select between classification (c), regression (r), and preference ranking (p) (default classification)
-c float
- C: trade-off between training error and margin (default [avg. x*x]^-1)
-w [0..] - epsilon width of tube for regression (default 0.1)
-j float - Cost: cost-factor, by which training errors on positive examples outweight errors on negative examples (default 1)
-b [0,1] - use biased hyperplane (i.e. x*w+b0) instead of unbiased hyperplane (i.e. x*w0) (default 1)
-i [0,1] - remove inconsistent training examples and retrain (default 0)
Performance estimation options:
-x [0,1] - compute leave-one-out estimates (default 0)
-o ]0..2] - value of rho for XiAlpha-estimator and for pruning leave-one-out computation (default 1.0)
-k [0..100] - search depth for extended XiAlpha-estimator default 0) Transduction options
-p [0..1] - fraction of unlabeled examples to be classified into the positive class (default is the ratio of positive and negative examples in the training data)
Kernel options:
-t int - type of kernel function:
0: linear (default)
1: polynomial (s a*b+c)^d
2: radial basis function exp(-gamma ||a-b||^2)
3: sigmoid tanh(s a*b + c)
4: user defined kernel from kernel.h
-d int - parameter d in polynomial kernel
-g float - parameter gamma in rbf kernel
-s float - parameter s in sigmoid/poly kernel
-r float - parameter c in sigmoid/poly kernel
-u string - parameter of user defined kernel Optimization options
-q [2..] - maximum size of QP-subproblems (default 10)
-n [2..q] - number of new variables entering the working set in each iteration (default n = q). Set n<q to prevent zig-zagging.
-m [5..] - size of cache for kernel evaluations in MB (default 40) The larger the faster...
-e float - eps: Allow that error for termination criterion [y [w*x+b] - 1] = eps (default 0.001)
-h [5..] - number of iterations a variable needs to be optimal before considered for shrinking (default 100)
-f [0,1] - do final optimality check for variables removed by shrinking. Although this test is usually positive, there is no guarantee that the optimum was found if the test is omitted. (default 1)
-y string -> if option is given, reads alphas from file with given and uses them as starting point. (default 'disabled')
-# int -> terminate optimization, if no progress after this number of iterations. (default 100000)
Output options:
-l char - file to write predicted labels of unlabeled examples into after transductive learning
-a char - write all alphas to this file after learning (in the same order as in the training set)
svm_learn [options] example_file model_file
bsub -q queueName svm_learn [options] example_file model_file -o queuefilename.out