name: RelevanceVectorMachine


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

#include <RelevanceVectorMachine.h>

ALGORITHM getAlgorithm();
boolean setAlgorithm(ALGORITHM arg);
IMPLEMENTATION getImplementation();
boolean setImplementation(IMPLEMENTATION 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 loadFeatures(Sdb& in_sdb, Sdb& out_sdb);
boolean train(RVMTrainData& tdata, ALGORITHM mode = DEF_ALGORITHM);
boolean writeModel();
quick start:

Sdb pos_sdb;
Sdb neg_sdb;

rvm.loadFeatures(pos_sdb, neg_sdb);
rvm.train(RVMTrainData& tdata);


This class computes the relevance vectors give a list of in-class and out-of-class observation using the relevance determination.


[1] M. E. Tipping, "Sparse Bayesian Learning and the Relevance Vector Machine," Journal of Machine Learning Research, vol 1, pp. 211-244, June 2001.

[2] David J. C. MacKay, "Probable Networks and Plausible Predictions - A Review of Practical Bayesian Methods for Supervised Neural Networks," Network: Computation in Neural Systems, vol. 6, no. 3, pp. 469-505, 1995.

[3] A. Faul and M. E. Tipping, "Analysis of Sparse Bayesian Learning," Proceedings of the 2001 Neural Information Processing Systems, 2001.

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private methods: