cross-entropy (CE)
A Cross Entropy Genetic Algorithm for m Machines No Wait Job ShopScheduling Problem
... methods. Cross entropy (CE), as a new metaheuristic, can be an alternative method to solve NWJSS ...of cross entropy with genetic algorithm (GA), called CEGA, on m-machines ...
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Learning Tetris Using the Noisy Cross-Entropy Method
... 2.3 The Cross-Entropy Method and Reinforcement Learning. Appli- cations of the CE method to RL include the parameter tuning of radial basis functions (Menache, Mannor, & Shimkin, 2005) and adaptation of ...
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Cross Entropy Method for Solving Generalized Orienteering Problem
... used cross entropy to solve combinatorial and rare-event, estimation [8], Derek Magee applied cross entropy on a sequential scheduling approach to combining multiple object classi- fiers [9], ...
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A cross entropy algorithm for continuous covering location problem
... CE leads to the definition of optimal updating rules for the density functions, and consequently to the generation of improved feasible vectors. The method terminates when convergence to a point in the feasible region is ...
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Multiclass Classification with Cross Entropy-Support Vector Machines
... the cross entropy method is applied to solve dual Lagrange SVM optimization problem to find the optimal or at least near optimal solution, which is Lagrange multipliers, ...
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Mutual Information and Cross Entropy Framework to Determine Relevant Gene Subset for Cancer Classification
... where S m 1 is the set of already selected (m-1) features. However, MIFS, MIFS-U and mRMR algorithms use incremental search approach which considers weighted average of only pair-wise correlation instead of considering ...
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Generalized decomposition and cross entropy methods for many-objective optimization
... Abstract—Decomposition-based algorithms for multi-objective optimization problems have increased in popularity in the past decade. Although their convergence to the Pareto optimal front (PF) is in several instances ...
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Extensions of the Cross-Entropy Method with Applications to Diffusion Processes and Portfolio Losses
... The second component of our algorithm consists of choosing the tilt parameters so as to ensure that the resulting IS measure has minimal Kullback-Liebler divergence (KLD) from the ideal (but impractical) zero-variance ...
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Marginal Likelihood Estimation with the Cross Entropy Method
... We consider an adaptive importance sampling approach to estimating the marginal likeli- hood, a quantity that is fundamental in Bayesian model comparison and Bayesian model averaging. This approach is motivated by the ...
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The Cross-Entropy Method for Policy Search in Decentralized POMDPs
... the Cross-Entropy (CE) method, a recently introduced method for combinatorial optimization, to Dec-POMDPs, resulting in a randomized (sampling-based) algorithm for approximately solving ...
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Some Order Preserving Inequalities for Cross Entropy and Kullback–Leibler Divergence
... This paper is organized as follows. After this introduction, in Section 2, we recapitulate the meaning of cross entropy and its relationship to likelihood and K-L divergence, and we obtain the first ...
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Language Model Weight Adaptation Based on Cross-entropy for Statistical Machine Translation
... the entropy on translations after adaptation of all dataset pairs, which is also listed in table ...the cross entropy usually changes according to the ratio of cross-entropy of ...
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Monotonic Estimation for Probability Distribution and Multivariate Risk Scales by Constrained Minimum Generalized Cross Entropy
... Results by (1.8) are the exact solution to the constrained optimization problem corresponding to (1.6) and are proved to be also the least squares estimates subject only to (1.4) (see Proposition 3.4), which links to ...
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NC-Cross Entropy Based MADM under Neutrosophic Cubic Set Environment
... Single valued neutrosophic cross-entropy for multi criteria decision making problems. Improved cross entropy measures of single valued neutrosophic sets and interval neutrosophic 633[r] ...
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Classification of Music Genre using Neural Networks with Cross-Entropy Optimization and Soft-Max Output
... In this paper, an abstract model to predict the genre of a music audio file is proposed (specifically a wave file).The output of the model is the probability distribution along the considered genres. A machine learning ...
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Cross entropy as a measure of musical contrast
... In a similar vein, Marsden [5] has questioned whether musical similarity is a definitive phenomenon or a product of interpretation. He reviews a number of approaches: the edit distance between melodies, possibly under ...
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Global Regularizer and Temporal aware Cross entropy for Skeleton based Early Action Recognition
... standard cross-entropy and a linearly increasing false positive loss, is introduced to pre- vent ambiguities in early action ...weighted cross-entropy is introduced for early action ...the ...
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Introduce a Novel PCA Method for Intuitionistic Fuzzy Sets Based on Cross Entropy
... In this paper, a new method for Principal Component Analysis in intuitionistic fuzzy situations has been proposed. This approach is based on cross entropy as an information index. This new me- thod is a ...
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An Affect-Rich Neural Conversational Model with Biased Attention and Weighted Cross-Entropy Loss
... In this paper, we propose an end-to-end open-domain neu- ral conversational model that produces affect-rich responses without performance degradation in language fluency. Our model leverages external word-VAD knowledge ...
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Dual Monolingual Cross Entropy Delta Filtering of Noisy Parallel Data
... We introduce a purely monolingual approach to filtering for parallel data from a noisy cor- pus in a low-resource scenario. Our work is inspired by Junczys-Dowmunt (2018), but we relax the requirements to allow for cases ...
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