Bayesian robust optimal linear filters
Signal Processing
An approach to the evaluation of the performance of a discrete classifier
Pattern Recognition Letters
The influence of prior knowledge on the expected performance of a classifier
Pattern Recognition Letters
Bayesian robustness for decision making problems: Applications in medical contexts
International Journal of Approximate Reasoning
Generalized SURE for exponential families: applications to regularization
IEEE Transactions on Signal Processing
Exact performance of error estimators for discrete classifiers
Pattern Recognition
IEEE Transactions on Information Theory - Special issue on information theory in molecular biology and neuroscience
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Brief paper: An analysis of the effects of spectral uncertainty on wiener filtering
Automatica (Journal of IFAC)
The rate of a class of random processes
IEEE Transactions on Information Theory
Robust detection of known signals
IEEE Transactions on Information Theory
Robust sequential detection of signals in noise
IEEE Transactions on Information Theory
On the p-point uncertainty class (Corresp.)
IEEE Transactions on Information Theory
Minimax Robust Quickest Change Detection
IEEE Transactions on Information Theory
Analytic Study of Performance of Error Estimators for Linear Discriminant Analysis
IEEE Transactions on Signal Processing
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Contemporary high-throughput technologies provide measurements of very large numbers of variables but often with very small sample sizes. This paper proposes an optimization-based paradigm for utilizing prior knowledge to design better performing classifiers when sample sizes are limited. We derive approximate expressions for the first and second moments of the true error rate of the proposed classifier under the assumption of two widely used models for the uncertainty classes: @e-contamination and p-point classes. The applicability of the approximate expressions is discussed by defining the problem of finding optimal regularization parameters through minimizing the expected true error. Simulation results using the Zipf model show that the proposed paradigm yields improved classifiers that outperform traditional classifiers which use only training data. Our application of interest involves discrete gene regulatory networks possessing labeled steady-state distributions. Given prior operational knowledge of the process, our goal is to build a classifier that can accurately label future observations obtained in the steady state by utilizing both the available prior knowledge and the training data. We examine the proposed paradigm on networks containing NF-@kB pathways, where it shows significant improvement in classifier performance over the classical data-only approach to classifier design. Companion website: http://gsp.tamu.edu/Publications/supplementary/shahrokh12a.