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A Tutorial on the CrossEntropy Method
, 2005
"... The crossentropy (CE) method is a new generic approach to combinatorial and multiextremal optimization and rare event simulation. The purpose of this tutorial is to give a gentle introduction to the CE method. We present the CE methodology, the basic algorithm and its modifications, and discuss a ..."
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Cited by 172 (18 self)
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The crossentropy (CE) method is a new generic approach to combinatorial and multiextremal optimization and rare event simulation. The purpose of this tutorial is to give a gentle introduction to the CE method. We present the CE methodology, the basic algorithm and its modifications, and discuss
Entropy method for the left tail
, 2006
"... When we use the entropy method to get the tail bounds, typically the left tail bounds are not good comparing with the right ones. Up to now this asymmetry has been observed many times. Surprisingly we find an entropy method for the left tail that works in the exactly same way that it works for the r ..."
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When we use the entropy method to get the tail bounds, typically the left tail bounds are not good comparing with the right ones. Up to now this asymmetry has been observed many times. Surprisingly we find an entropy method for the left tail that works in the exactly same way that it works
The CrossEntropy Method for Optimization
"... The crossentropy method is a versatile heuristic tool for solving difficult estimation and optimization problems, based on Kullback–Leibler (or crossentropy) minimization. As an optimization method it unifies many existing populationbased optimization heuristics. In this chapter we show how the cr ..."
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Cited by 4 (2 self)
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The crossentropy method is a versatile heuristic tool for solving difficult estimation and optimization problems, based on Kullback–Leibler (or crossentropy) minimization. As an optimization method it unifies many existing populationbased optimization heuristics. In this chapter we show how
Empirical Maximum Entropy Methods
"... Abstract. A method, which we suggest to call the Empirical Maximum Entropy method, is implicitly present at Maximum Entropy Empirical Likelihood method, as its special, nonparametric case. From this vantage point the entropybased empirical approach to estimation is surveyed. ..."
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Abstract. A method, which we suggest to call the Empirical Maximum Entropy method, is implicitly present at Maximum Entropy Empirical Likelihood method, as its special, nonparametric case. From this vantage point the entropybased empirical approach to estimation is surveyed.
Deconvolution of astronomical images using the multiscale maximum entropy method
 Astronomy and Astrophysics, Suppl. Ser
, 1996
"... entropy method ..."
The cross entropy method for classification
"... We consider support vector machines for binary classification. As opposed to most approaches we use the number of support vectors (the “L0 norm”) as a regularizing term instead of the L1 or L2 norms. In order to solve the optimization problem we use the cross entropy method to search over the possib ..."
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Cited by 2 (0 self)
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We consider support vector machines for binary classification. As opposed to most approaches we use the number of support vectors (the “L0 norm”) as a regularizing term instead of the L1 or L2 norms. In order to solve the optimization problem we use the cross entropy method to search over
Generalized CrossEntropy Methods
"... The crossentropy and minimum crossentropy methods are wellknown Monte Carlo simulation techniques for rareevent probability estimation and optimization. In this paper we investigate how these methods can be extended to provide a general nonparametric crossentropy framework based on φdivergenc ..."
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Cited by 2 (0 self)
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The crossentropy and minimum crossentropy methods are wellknown Monte Carlo simulation techniques for rareevent probability estimation and optimization. In this paper we investigate how these methods can be extended to provide a general nonparametric crossentropy framework based on φ
The CrossEntropy Method for Estimation
"... This chapter describes how difficult statistical estimation problems can often be solved efficiently by means of the crossentropy (CE) method. The CE method can be viewed as an adaptive importance sampling procedure that uses the crossentropy or Kullback–Leibler divergence as a measure of closenes ..."
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This chapter describes how difficult statistical estimation problems can often be solved efficiently by means of the crossentropy (CE) method. The CE method can be viewed as an adaptive importance sampling procedure that uses the crossentropy or Kullback–Leibler divergence as a measure
A Maximum Entropy approach to Natural Language Processing
 COMPUTATIONAL LINGUISTICS
, 1996
"... The concept of maximum entropy can be traced back along multiple threads to Biblical times. Only recently, however, have computers become powerful enough to permit the widescale application of this concept to real world problems in statistical estimation and pattern recognition. In this paper we des ..."
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Cited by 1337 (5 self)
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describe a method for statistical modeling based on maximum entropy. We present a maximumlikelihood approach for automatically constructing maximum entropy models and describe how to implement this approach efficiently, using as examples several problems in natural language processing.
EntropyBased Algorithms For Best Basis Selection
 IEEE Transactions on Information Theory
, 1992
"... pretations (position, frequency, and scale), and we have experimented with featureextraction methods that use bestbasis compression for frontend complexity reduction. The method relies heavily on the remarkable orthogonality properties of the new libraries. It is obviously a nonlinear transformat ..."
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Cited by 665 (20 self)
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pretations (position, frequency, and scale), and we have experimented with featureextraction methods that use bestbasis compression for frontend complexity reduction. The method relies heavily on the remarkable orthogonality properties of the new libraries. It is obviously a nonlinear
Results 1  10
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