************************************************************************
* LARANK : Online solver for multiclass Support Vector Machines.       *
* (see Bordes et al., "Solving MultiClass Support Vector Machines with *
* LaRank" published in Proceedings of ICML'07, for more details)       *  
************************************************************************



** LICENSE **:

This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation;



** COMPILATION **: 

This library has been implemented in C++. Simply typing make should
generate the la_rank_learn and la_rank_classify binaries.



** USAGE **: 

The C++ library implements the kernel cache and the basic operations. 
Two additional programs, la_rank_learn and la_rank_classify can be 
used to run experiments. la_rank_learn learns and stores models for multiclass 
classification with LaRank. la_rank_classify uses a model learned 
with LaRank to make predictions.

Typing la-rank_learn and/or la_rank_classify with no argument should
producethe following helps.


LA_RANK_LEARN: learns models for multiclass classification with the 'LaRank algorithm'.

Usage: la_rank_learn [options] training_set_file model_file
options:
-c cost : set the parameter C (default 1)
-e tau : threshold determining tau-violating pairs of coordinates (default 1e-4)
-t kernel function (default 0):
        0 linear : K(X,X')=X*X'
        1 polynomial : K(X,X')=(g*X*X'+c0)^d
        2 rbf : K(X,X')=exp(-g*||X-X'||^2)
-g gamma : coefficient for polynomial and rbf kernels (default 1)
-d degree of polynomial kernel (default 2)
-b c0 coefficient for polynomial kernel (default 0)
-k cache size : in MB (default 64)
-m mode : set the learning mode (default 0)
         0: online learning
         1: batch learning (stopping criteria: duality gap < C)
-v verbosity degree : display informations every v % of the training set size (default 10)


LA_RANK_CLASSIFY: uses models learned with the 'LaRank algorithm' for multiclass classification to make prediction.

Usage: la_rank_classify [options] training_set_file testing_set_file model_file
options:
-t kernel function (default 0):
        0 linear : K(X,X')=X*X'
        1 polynomial : K(X,X')=(g*X*X'+c0)^d
        2 rbf : K(X,X')=exp(-g*||X-X'||^2)
-g gamma : coefficent for polynomial and rbf kernels (default 1)
-d degree of polynomial kernel (default 2)
-b c0 coefficient for polynomial kernel (default 0)



** DATA FILE FORMAT **

The programs uses the so called LibSVM/SVMlight/SVMstruct data
format. Each example is represented by a line in the following format:
<line>    = <target> <feature>:<value> ... <feature>:<value> 
<target>  = <int>
<feature> = <integer> 
<value>   = <float>



** Copyright (C) 2008- Antoine Bordes **