A model selection algorithm for a posteriori probability estimation with neural networks

TitleA model selection algorithm for a posteriori probability estimation with neural networks
Publication TypeJournal Article
Year of Publication2005
AuthorsArribas, J. I., and J. Cid-Sueiro
JournalIEEE Transactions on Neural Networks
Volume16
Pagination799-809
ISSN10459227
Keywordsalgorithm, Algorithms, article, artificial neural network, Automated, automated pattern recognition, Biological, biological model, Breast Neoplasms, breast tumor, classification, cluster analysis, computer analysis, Computer-Assisted, computer assisted diagnosis, Computer simulation, Computing Methodologies, decision support system, Decision Support Techniques, Diagnosis, Estimation, evaluation, Expectation-maximization, Generalized Softmax Perceptron (GSP), human, Humans, mathematical computing, Mathematical models, methodology, Models, Model selection, Neural networks, Neural Networks (Computer), Numerical Analysis, Objective function, Pattern recognition, Posterior probability, Probability, Statistical, statistical model, statistics, Stochastic Processes
Abstract

This paper proposes a novel algorithm to jointly determine the structure and the parameters of a posteriori probability model based on neural networks (NNs). It makes use of well-known ideas of pruning, splitting, and merging neural components and takes advantage of the probabilistic interpretation of these components. The algorithm, so called a posteriori probability model selection (PPMS), is applied to an NN architecture called the generalized softmax perceptron (GSP) whose outputs can be understood as probabilities although results shown can be extended to more general network architectures. Learning rules are derived from the application of the expectation-maximization algorithm to the GSP-PPMS structure. Simulation results show the advantages of the proposed algorithm with respect to other schemes. © 2005 IEEE.

URLhttp://www.scopus.com/inward/record.url?eid=2-s2.0-23044459586&partnerID=40&md5=f00e7d86a625cfc466373a2a938276d0
DOI10.1109/TNN.2005.849826