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454,546
OPTIMIZATION CRITERION
, 2000
"... Probability models with discrete random variables are often used for probabilistic inference and decision support. A fundamental issue lies in the choice and the validity of the probability model. An information theoreticbased approach for probability model selection is discussed. It will be shown ..."
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that the problem of probability model selection can be formulated as an optimization problem with linear (in)equality constraints and a nonlinear objective function. An algorithm for model discovery/selection based on a primal – dual formulation similar to that of the interior point method is presented
An Optimality Criterion For Arithmetic Of Complex Sets
, 1999
"... Uncertainty of measuring complexvalued physical quantities can be described by complex sets. These sets can have complicated shapes, so we would like to find a good approximating family of sets. Which approximating family is the best? We reduce the corresponding optimization problem to a geometric ..."
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Uncertainty of measuring complexvalued physical quantities can be described by complex sets. These sets can have complicated shapes, so we would like to find a good approximating family of sets. Which approximating family is the best? We reduce the corresponding optimization problem to a geometric
An optimization criterion for generalized discriminant analysis on undersampled problems
 IEEE Trans. Pattern Analysis and Machine Intelligence
, 2004
"... Abstract—An optimization criterion is presented for discriminant analysis. The criterion extends the optimization criteria of the classical Linear Discriminant Analysis (LDA) through the use of the pseudoinverse when the scatter matrices are singular. It is applicable regardless of the relative size ..."
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Cited by 52 (9 self)
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Abstract—An optimization criterion is presented for discriminant analysis. The criterion extends the optimization criteria of the classical Linear Discriminant Analysis (LDA) through the use of the pseudoinverse when the scatter matrices are singular. It is applicable regardless of the relative
A Proof of the LOptimality Criterion Theorem
, 2004
"... An alternative proof is given to the theorem of Loptimality criterion on the basis of a property of an L\convex function labelled (L\APR[Z]). Theorem 1 (Loptimality criterion [1, Th.5.12], [2, Th.7.14]) For an L\convex function g ∈ L\[Z → R] and p ∈ dom g, we have g(p) ≤ g(q) ( ∀ q ∈ ZV) ⇐ ⇒ g ..."
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An alternative proof is given to the theorem of Loptimality criterion on the basis of a property of an L\convex function labelled (L\APR[Z]). Theorem 1 (Loptimality criterion [1, Th.5.12], [2, Th.7.14]) For an L\convex function g ∈ L\[Z → R] and p ∈ dom g, we have g(p) ≤ g(q) ( ∀ q ∈ ZV
Toward optimal feature selection
 In 13th International Conference on Machine Learning
, 1995
"... In this paper, we examine a method for feature subset selection based on Information Theory. Initially, a framework for de ning the theoretically optimal, but computationally intractable, method for feature subset selection is presented. We show that our goal should be to eliminate a feature if it g ..."
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Cited by 480 (9 self)
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if it gives us little or no additional information beyond that subsumed by the remaining features. In particular, this will be the case for both irrelevant and redundant features. We then give an e cient algorithm for feature selection which computes an approximation to the optimal feature selection criterion
Least squares quantization in pcm.
 Bell Telephone Laboratories Paper
, 1982
"... AbstractIt has long been realized that in pulsecode modulation (PCM), with a given ensemble of signals to handle, the quantum values should be spaced more closely in the voltage regions where the signal amplitude is more likely to fall. It has been shown by Panter and Dite that, in the limit as t ..."
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Cited by 1362 (0 self)
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conditions are found that the quanta and associated quantization intervals of an optimum finite quantization scheme must satisfy. The optimization criterion used is that the average quantization noise power be a minimum. It is shown that the result obtained here goes over into the Panter and Dite result
Optimizationseeking Experimentations: Design of an RLcircuit Via the Vsoptimality Criterion
"... In this paper we explore the Vsoptimality criterion that was proposed in Ginsburg and BenGal (IIE Trans. 2006; 38:445– 461) as a new designofexperiment (DOE) alphabetic optimality criterion. The Vsoptimality criterion seeks to minimize the variance of the optimal solution of an empirically fitt ..."
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In this paper we explore the Vsoptimality criterion that was proposed in Ginsburg and BenGal (IIE Trans. 2006; 38:445– 461) as a new designofexperiment (DOE) alphabetic optimality criterion. The Vsoptimality criterion seeks to minimize the variance of the optimal solution of an empirically
MINIMAXITY OF RANDOMIZED OPTIMAL DESIGNS WITH RESPECT TO A GENERAL OPTIMALITY CRITERION
"... SUMMARY. Uniform randomization of a design that is optimal under an ideal model, with respect to a general optimality criterion, is shown to be minimax under a larger model. This generalizes a result due to Hooper (1989). 1. ..."
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SUMMARY. Uniform randomization of a design that is optimal under an ideal model, with respect to a general optimality criterion, is shown to be minimax under a larger model. This generalizes a result due to Hooper (1989). 1.
LINEAR CONTROL THEORY WITH AN H∞ OPTIMALITY CRITERION
, 1987
"... This expository paper sets out the principal results in H∞ control theory in the context of continuoustime linear systems. The focus is on the mathematical theory rather than computational methods. ..."
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Cited by 18 (0 self)
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This expository paper sets out the principal results in H∞ control theory in the context of continuoustime linear systems. The focus is on the mathematical theory rather than computational methods.
Results 1  10
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454,546