Titlebook: Artificial Neural Networks - ICANN 2001; International Confer Georg Dorffner,Horst Bischof,Kurt Hornik Conference proceedings 2001 Springer

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書目名稱Artificial Neural Networks - ICANN 2001讀者反饋




書目名稱Artificial Neural Networks - ICANN 2001讀者反饋學(xué)科排名

Herbivorous

Brent Kawahara,Hector Estrada,Luke S. Leetional invariance can be based, and try to delimit the conditions under which each of them acts. We find out that, surprisingly, some of the most popular neural learning methods, such as weight-decay and input noise addition, exhibit this interesting property.

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愛哭

STALE

https://doi.org/10.1057/978-1-349-93268-9 weight in the output layer is derived as a nonlinear function of the training data moments. The experimental results, using one- and two-dimensional simulated data and different polynomial orders, show that the classification rate of the polynomial densities is very close to the optimum rate.

儀式

Neural Learning Invariant to Network Size Changestional invariance can be based, and try to delimit the conditions under which each of them acts. We find out that, surprisingly, some of the most popular neural learning methods, such as weight-decay and input noise addition, exhibit this interesting property.

尖牙

Etymology

Discriminative Dimensionality Reduction Based on Generalized LVQionality reduction in feature extraction. Experimental results reveal that the training of both a feature transformation matrix and reference vectors by GLVQ is superior to that by principal component analysis in terms of dimensionality reduction.

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甜得發(fā)膩

Fast Curvature Matrix-Vector ProductsFisher information matrices with arbitrary vectors, using techniques similar to but even cheaper than the fast Hessian-vector product [.]. The stability of SMD [.,.,.,.], a learning rate adaptation method that uses curvature matrix-vector products, improves when the extended Gauss-Newton matrix is substituted for the Hessian.