Results 131 to 140 of about 204,288 (176)

Cross-entropy motion planning

The International Journal of Robotics Research, 2012
This paper is concerned with motion planning for non-linear robotic systems operating in constrained environments. A method for computing high-quality trajectories is proposed building upon recent developments in sampling-based motion planning and stochastic optimization.
openaire   +2 more sources

A Cross-Entropy Scheme for Mixtures

ACM Transactions on Modeling and Computer Simulation, 2015
We discuss how to generalize the classic cross-entropy method in the case where a family of mixture distributions, such as the mixture of multiple Gaussian modes, is used as an importance sampling distribution. A new iterative cross-entropy scheme, based on the idea of the EM method, is proposed to overcome the challenge of deciding the optimal weights
Wang, Hui, Zhou, Xiang
openaire   +2 more sources

Minimum cross-entropy threshold selection

Pattern Recognition, 1996
Thresholding is a common and easily implemented form of image segmentation. Many methods of automatic threshold selection based on the optimization of some discriminant function have been proposed. Such functions often take the form of a metric distance or similarity measure between the original image and the segmented result. A non-metric measure, the
A.D. Brink, N.E. Pendock
openaire   +1 more source

Cross-Entropy Based Ensemble Classifiers

2016
Multiple classification rules are simultaneously identified by applying the Cross-Entropy method to the maximization of accuracy measures in a supervised learning context. Optimal ensembles of rules are searched through stochastic traversals of the rule space.
openaire   +2 more sources

Properties of cross-entropy minimization

IEEE Transactions on Information Theory, 1981
The principle of minimum cross-entropy (minimum directed divergence, minimum discrimination information) is a general method of inference about an unknown probability density when there exists a prior estimate of the density and new information in the form of constraints on expected values.
Shore, John E., Johnson, Rodney W.
openaire   +1 more source

LARGE LANGUAGE MODELS: COMPARISON OF CROSS-ENTROPY AND BINARY CROSS-ENTROPY LOSS

HUMAN. ENVIRONMENT. TECHNOLOGIES. Proceedings of the Students International Scientific and Practical Conference
The paper explores Large Language Model (LLM) training on custom datasets for classification microservice development. As training general purpose models for every possible situation is not feasible on smaller scale, because of limitations of computation power, usage of smaller model architectures, such as NanoGPT for training LLM model for specific ...
Apeinans, Ilmars, Kodors, Sergejs, Zarembo, Imants   +2 more
openaire   +2 more sources

Cross-entropy and iterative decoding

IEEE Transactions on Information Theory, 1998
Summary: In this correspondence, the relationship between iterative decoding and techniques for minimizing cross-entropy is explained. It is shown that minimum cross-entropy (MCE) decoding is an optimal lossless decoding algorithm but its complexity limits its practical implementation.
Moher, Michael, Gulliver, T. Aaron
openaire   +2 more sources

Using cross-entropy for satisfiability

Proceedings of the 28th Annual ACM Symposium on Applied Computing, 2013
This paper proposes a novel approach to SAT solving by using the cross-entropy method for optimization. It introduces an extension of the Boolean satisfiability setting to a multi-valued framework, where a probability space is induced over the set of all possible assignments.
Hana Chockler, Alexander Ivrii, Arie Matsliah, Simone Fulvio Rollini, Natasha Sharygina   +4 more
openaire   +1 more source

Cross entropy based spectrum sensing

2010 IEEE 12th International Conference on Communication Technology, 2010
In this paper, we propose a spectrum sensing method by exploiting the cross entropy. Spectrum sensing is a critical step for Cognitive Radio (CR) to have a successful communication. Intensive interests have been paid to the improvement of spectrum sensing. However, the relationship of previous and current detected data sets of Primary User (PU) has not
null Junrong Gu, null Wenlong Liu, null Sung Jeen Jang, null Jae Moung Kim   +3 more
openaire   +1 more source

Combinatorial Optimization via Cross-Entropy

2004
In this chapter we show how the CE method can be easily transformed into an efficient and versatile randomized algorithm for solving optimization problems, in particular combinatorial optimization problems.
Reuven Y. Rubinstein, Dirk P. Kroese
openaire   +1 more source