An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Partial likelihood for estimation of multi-class posterior probabilities
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
Conditional distribution learning with neural networks and itsapplication to channel equalization
IEEE Transactions on Signal Processing
IEEE Transactions on Image Processing
Partial likelihood for online order selection
Signal Processing - Special issue: Information theoretic signal processing
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We use partial likelihood (PL) theory to introduce a general probabilistic framework for the design and analysis of neural classifiers. The formulation allows for the training samples used in the design to have correlations in time, and for use of a wide range of neural network probability models including recurrent structures. We use PL theory to establish a fundamental information-theoretic connection, show the equivalence of likelihood maximization and relative entropy minimization, without making the common assumptions of independent training samples and true distribution information. We use this result to construct the information geometry of partial likelihood and derive the information geometric e- and m-projection (em) algorithm for class conditional density modeling by finite normal mixtures. We demonstrate the successful application of the algorithm by a channel equalization example and give simulation results to show the efficiency of the scheme.