name: SupportVectorMachine


g++ [flags ...] file ... -l /isip/tools/lib/$ISIP_BINARY/lib_pr.a

#include <SupportVectorMachine.h>

ALGORITHM getAlgorithm();
boolean setAlgorithm(ALGORITHM arg);
IMPLEMENTATION getImplementation();
boolean setImplementation(IMPLEMENTATION arg);
boolean setPenalty(float arg);
boolean setEpsilon(float arg);
boolean setTolerance (float arg);
boolean setDegree(float arg);
boolean setKappa(float arg);
boolean setDelta(float arg);
boolean setGamma(float arg);
boolean setVerbosity(Integral::DEBUG verbosity);
boolean setOutputFile(Filename& arg);
boolean setOutputType(TYPE arg);
boolean init();
boolean loadRawFeatures(Filename& arg);
boolean loadFeatures(Sdb& in_sdb, Sdb& out_sdb);
boolean train();
boolean writeModel();
quick start:

Sdb pos_sdb;
Sdb neg_sdb;

svm.loadFeatures(pos_sdb, neg_sdb);


This class computes the support vectors give a list of in-class and out-of-class observation using the Sequential Minimization Optimization (SMO) algorithm.


[1] V. N. Vapnik, "The Nature of Statistical Learning Theory", Springer-Verlag, New York, 1995.

[2] J. C. Platt, "Fast Training of Support Vector Machines using Sequential Minimal Optimization". In B. Scholkopf, C. J. C. Burges and A. J. Smola, editors, "Advances in Kernel Methods - Support Vector Learning", pp 185-208, MIT Press, 1998.

[3] N. Cristianini, J. Shawe-Taylor, "An Introduction to Support Vector Machines", Cambridge Press, 2000.

[4] C. J. C. Burges, "A Tutorial on Support Vector Machines for Pattern Recognition". Available on-line at (Microsoft Research):


public constants:

error codes:

protected data:

required public methods:

class-specific public methods:

private methods: