Titlebook: Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003; Joint International Okyay Kaynak,Ethem Alpaydin,Lei Xu C

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Femish

https://doi.org/10.1007/978-3-658-37586-7learning 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 results.

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Rafaela Sales Goulart,Fabiana Lopes da Cunharrelatedness between the independent components prevents them from converging to the same optimum. A simple and popular way of achieving decorrelation between recovered independent components is a deflation scheme based on a Gram-Schmidt-like decorrelation

步履蹒跚

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 results.

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旧式步枪

Adaptive Hopfield Networktion mechanism” to guide the neural search process towards high-quality solutions for large-scale static optimization problems. Specifically, a novel methodology that employs gradient-descent in the error space to adapt weights and constraint weight parameters in order to guide the network dynamics

Cerebrovascular

Effective Pruning Method for a Multiple Classifier System Based on Self-Generating Neural Networksrks (SGNN) are one of the suitable base-classifiers for MCS because of their simple setting and fast learning. However, the computational cost of the MCS increases in proportion to the number of SGNN. In this paper, we propose a novel pruning method for the structure of the SGNN in the MCS. Experime

Hirsutism

Structural Bias in Inducing Representations for Probabilistic Natural Language Parsingries, which are used to estimate probabilities for parser decisions. This induction process is given domain-specific biases by matching the flow of information in the network to structural locality in the parse tree, without imposing any independence assumptions. The parser achieves performance on t

GONG

Independent Component Analysis Minimizing Convex Divergencethis information measure is minimized to derive new ICA algorithms. Since the convex divergence includes logarithmic information measures as special cases, the presented method comprises faster algorithms than existing logarithmic ones. Another important feature of this paper’s ICA algorithm is to a

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