A General Probabilistic Formulation for Supervised Neural Classifiers
Journal of VLSI Signal Processing Systems
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
Sequential Monte Carlo for model selection and estimation of neural networks
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 06
Partial likelihood for signal processing
IEEE Transactions on Signal Processing
Conditional distribution learning with neural networks and itsapplication to channel equalization
IEEE Transactions on Signal Processing
A model selection rule for sinusoids in white Gaussian noise
IEEE Transactions on Signal Processing
Blind channel approximation: effective channel order determination
IEEE Transactions on Signal Processing
Analysis of the performance and sensitivity ofeigendecomposition-based detectors
IEEE Transactions on Signal Processing
On the behavior of information theoretic criteria for model orderselection
IEEE Transactions on Signal Processing
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Partial likelihood (PL) is a flexible framework for adaptive nonlinear signal processing allowing the use of a wide class of nonlinear structures--probability models--as filters. PL maximization has been shown to be equivalent to relative entropy minimization for the general case of time-dependent observations and its large sample properties have been established. In this paper, we use these properties to derive an information-theoretic criterion for order selection-- the penalized partial likelihood (PPL) criterion,--for the general case of dependent observations. We then consider nonlinear signal processing by conditional finite normal mixtures as an example, a problem for which true order selection is particularly important. For this case, in which the PL coincides with the usual likelihood formulation, we present a formulation for online order selection by eliminating the need to store all data samples up to the current time. We demonstrate the successful application of the PPL criterion and its online implementation for the equalization problem by simulation examples.