Estimates of constrained multi-class a posteriori probabilities in time series problems with neural networks

TitleEstimates of constrained multi-class a posteriori probabilities in time series problems with neural networks
Publication TypeConference Paper
Year of Publication1999
AuthorsArribas, J. I., J. Cid-Sueiro, T. Adali, H. Ni, B. Wang, and A. R. Figueiras-Vidal
Conference NameProceedings of the International Joint Conference on Neural Networks
PublisherIEEE, United States
Conference LocationWashington, DC, USA
KeywordsApproximation theory, Computer simulation, Constraint theory, Data structures, Joint network-data density estimation (JNDDE), Mathematical models, Multi-class a posteriori probabilities, Neural networks, Partial likelihood estimation (PLE), Probability density function, Regression analysis
Abstract

In time series problems, where time ordering is a crucial issue, the use of Partial Likelihood Estimation (PLE) represents a specially suitable method for the estimation of parameters in the model. We propose a new general supervised neural network algorithm, Joint Network and Data Density Estimation (JNDDE), that employs PLE to approximate conditional probability density functions for multi-class classification problems. The logistic regression analysis is generalized to multiple class problems with softmax regression neural network used to model the a-posteriori probabilities such that they are approximated by the network outputs. Constraints to the network architecture, as well as to the model of data, are imposed, resulting in both a flexible network architecture and distribution modeling. We consider application of JNDDE to channel equalization and present simulation results.

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