Nerve-Block 发表于 2025-3-28 16:45:48
Fast and Efficient Training of RBF Networkstic, iterative training algorithms (e.g. gradient-based or second-order techniques) or clustering methods in combination with a linear optimisation technique (e.g. c-means and singular value decomposition for a linear least-squares problem) are applied to find the parameters (centres, radii and weigCarbon-Monoxide 发表于 2025-3-28 22:35:30
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Differential ICAation . In this paper we present an ICA algorithm which employs differential learning, thus named as .. We derive a differential ICA algorithm in the framework of maximum likelihood estimation and random walk model. Algorithm derivation using the natural gradient and local stability analysis are向外 发表于 2025-3-29 03:53:07
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Optimal Hebbian Learning: A Probabilistic Point of Viewlearning rule from a probabilistic optimality criterion. Our approach allows us to obtain quantitative results in terms of a learning window. This is done by maximising a given likelihood function with respect to the synaptic weights. The resulting weight adaptation is compared with experimental resNonflammable 发表于 2025-3-29 19:32:49
Competitive Learning by Information Maximization: Eliminating Dead Neurons in Competitive Learning the lateral inhibition is used. Instead, the new method is based upon mutual information maximization between input patterns and competitive units. In maximizing mutual information, the entropy of competitive units is increased as much as possible. This means that all competitive units must equally斥责 发表于 2025-3-29 20:23:16
http://reply.papertrans.cn/17/1627/162670/162670_48.pngEnzyme 发表于 2025-3-30 00:11:14
Finite Mixture Model of Bounded Semi-naive Bayesian Networks Classifiershows a good performance in classification tasks. However, the traditional SNBs can only combine two attributes into a combined attribute. This inflexibility together with its strong independency assumption may generate inaccurate distributions for some datasets and thus may greatly restrict the cla抵制 发表于 2025-3-30 05:29:47
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