The main problem of segmentation in spectral images that containing mixed pixels is addressed. Linear spectral unmixing is a procedure by which mixed pixels are decomposed into a collection of pure spectra, or endmembers, with their corresponding proportions, or abundances. Markov random field (MRF) is used to model the spatial correlation between pixels and segment the image into multiple clas...

Although the concept of entropy is originated from thermodynamics, its concepts and relevant principles, especially the principles of maximum entropy and minimum cross-entropy, have been extensively applied in finance. In this paper, we review the concepts and principles of entropy, as well as their applications in the field of finance, especially in portfolio selection and asset pricing. Furth...

In this paper, we examine the CE method in the broad context of Monte Carlo Optimization (MCO) [Ermoliev and Norkin, 1998, Robert and Casella, 2004] and Parametric Learning (PL), a type of machine learning. A well-known overarching principle used to improve the performance of many PL algorithms is the bias-variance tradeoff [Wolpert, 1997]. This tradeoff has been used to improve PL algorithms r...

For playing the game of Tetris well, training a controller by the cross-entropy method seems to be a viable way (Szita and Lőrincz, 2006; Thiery and Scherrer, 2009). We consider this method to tune an evaluation-based one-piece controller as suggested by Szita and Lőrincz and we introduce some improvements. In this context, we discuss the influence of the noise, and we perform experiments with ...

The cross-entropy method [2] is a simple but efficient method for global optimization. In this paper we provide two online variants of the basic CEM, together with a proof of convergence.

This paper proposes a way to combine the Mesh Adaptive Direct Search (MADS) algorithm with Cross-Entropy (CE) method for nonsmooth constrained optimization. The CE is used as an exploration step by MADS algorithm. result of this combination retains convergence properties and allows efficient in order move away from local minima. samples trial points according multivariate normal distribution wh...

Wireless sensor network localization technique remains an open research topic due to its challenges on reducing the location estimation error and cost of the localization algorithm itself. For a large mobile network localization cost becomes increasingly important due to the exponential increment of the algorithmic cost. Conversely sacrificing localization accuracy to a great extent is not acce...