Results 11 to 20 of about 204,288 (176)
Cross-Entropy Clustering [PDF]
Pattern Recognition, 2012 We construct a cross-entropy clustering (CEC) theory which finds the optimal number of clusters by automatically removing groups which carry no information. Moreover, our theory gives simple and efficient criterion to verify cluster validity.
Spurek, Przemysław, Tabor, Jacek
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Parallel Cross-Entropy Optimization [PDF]
2007 Winter Simulation Conference, 2007 The cross-entropy (CE) method is a modern and effective optimization method well suited to parallel implementations. There is a vast array of problems today, some of which are highly complex and can take weeks or even longer to solve using current ...
Evans, Gareth +2 more
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Semiparametric Cross Entropy for rare-event simulation [PDF]
Journal of Applied Probability, 2013 The Cross Entropy method is a well-known adaptive importance sampling method for rare-event probability estimation, which requires estimating an optimal importance sampling density within a parametric class.
Botev, Z. I. +2 more
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Deconstructing Cross-Entropy for Probabilistic Binary Classifiers [PDF]
Entropy, 2018 In this work, we analyze the cross-entropy function, widely used in classifiers both as a performance measure and as an optimization objective. We contextualize cross-entropy in the light of Bayesian decision theory, the formal probabilistic framework ...
Daniel Ramos +3 more
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Streaming generalized cross entropy [PDF]
Soft Computing, 2019 We propose a new method to combine adaptive processes with a class of entropy estimators for the case of streams of data. Starting from a first estimation obtained from a batch of initial data, model parameters are estimated at each step by combining the prior knowledge with the new observation (or a block of observations).
Angelelli M., Ciavolino E., Pasca P.
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Entropy, 2023 Deep neural networks have made great achievements in remote sensing image analyses; however, previous studies have shown that deep neural networks exhibit incredible vulnerability to adversarial examples, which raises concerns about regional safety and ...
Qingan Da +6 more
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Channels’ Confirmation and Predictions’ Confirmation: From the Medical Test to the Raven Paradox
Entropy, 2020 After long arguments between positivism and falsificationism, the verification of universal hypotheses was replaced with the confirmation of uncertain major premises. Unfortunately, Hemple proposed the Raven Paradox.
Chenguang Lu
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Mathematics, 2022 Image segmentation is a key stage in image processing because it simplifies the representation of the image and facilitates subsequent analysis. The multi-level thresholding image segmentation technique is considered one of the most popular methods ...
Qingxin Liu +4 more
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Complex Contagion Features without Social Reinforcement in a Model of Social Information Flow
Entropy, 2020 Contagion models are a primary lens through which we understand the spread of information over social networks. However, simple contagion models cannot reproduce the complex features observed in real-world data, leading to research on more complicated ...
Tyson Pond +4 more
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Stop criteria for retransmission termination in soft-combining algorithms
Journal of Telecommunications and Information Technology, 2001 Soft-combining algorithms use retransmissions of the same codeword to improve the reliability of communication over very noisy channels. In this paper, soft-outputs from a maximum a posteriori (MAP) decoder are used as a priori information for decoding ...
Manora Caldera
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