On Input Selection with Reversible Jump Markov Chain Monte Carlo Sampling

In this paper we will treat input selection for a radial basis function (RBF) like classifier within a Bayesian framework. We approximate the a-posteriori distribution over both model coefficients and input subsets by samples drawn with Gibbs updates and reversible jump moves. Using some public datasets, we compare the classification accuracy of the method with a conventional ARD scheme. These datasets are also used to infer the a-posteriori probabilities of different input subsets.

[1]  J. Davenport Editor , 1960 .

[2]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[3]  Josef Kittler,et al.  Pattern recognition : a statistical approach , 1982 .

[4]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Editors , 1986, Brain Research Bulletin.

[6]  Hans G. C. Tråvén,et al.  A neural network approach to statistical pattern classification by 'semiparametric' estimation of probability density functions , 1991, IEEE Trans. Neural Networks.

[7]  Michael I. Jordan,et al.  Supervised learning from incomplete data via an EM approach , 1993, NIPS.

[8]  Michael I. Miller,et al.  REPRESENTATIONS OF KNOWLEDGE IN COMPLEX SYSTEMS , 1994 .

[9]  Terrence J. Sejnowski,et al.  A Mixture Model System for Medical and Machine Diagnosis , 1994, NIPS.

[10]  B. Carlin,et al.  Bayesian Model Choice Via Markov Chain Monte Carlo Methods , 1995 .

[11]  P. Green Reversible jump Markov chain Monte Carlo computation and Bayesian model determination , 1995 .

[12]  Walter R. Gilks,et al.  Bayesian model comparison via jump diffusions , 1995 .

[13]  Geoffrey E. Hinton,et al.  Bayesian Learning for Neural Networks , 1995 .

[14]  Sylvia Richardson,et al.  Markov Chain Monte Carlo in Practice , 1997 .

[15]  P. Green,et al.  Corrigendum: On Bayesian analysis of mixtures with an unknown number of components , 1997 .

[16]  C. C. Homes,et al.  Bayesian Radial Basis Functions of Variable Dimension , 1998, Neural Computation.