Titlebook: Artificial Neural Networks - ICANN 2006; 16th International C Stefanos D. Kollias,Andreas Stafylopatis,Erkki Oja Conference proceedings 200

單調(diào)性

場所

Torrid

Building Ensembles of Neural Networks with Class-Switchingon of the training data. The perturbation consists in switching the class labels of a subset of training examples selected at random. Experiments on several UCI and synthetic datasets show that these class-switching ensembles can obtain improvements in classification performance over both individual networks and bagging ensembles.

Intractable

花爭吵

Jan Augustin,Gert Middelhoff,W. Virgil Brown, even fast variable selection methods lead to high computational load. However, spectra are generally smooth and can therefore be accurately approximated by splines. In this paper, we propose to use a B-spline expansion as a pre-processing step before variable selection, in which original variables

憤慨點吧

防水

https://doi.org/10.1007/978-3-642-66302-4ancy filter using mutual information between regression and target variables. We introduce permutation tests to find statistically significant relevant and redundant features. Second, a wrapper searches for good candidate feature subsets by taking the regression model into account. The advantage of

和諧

Günther Dietze,Hans-Ulrich H?ringparameters coming from irrelevant or redundant variables are eliminated. Information theory provides a robust theoretical framework for performing input variable selection thanks to the concept of mutual information. Nevertheless, for continuous variables, it is usually a more difficult task to dete

艦旗

V洗浴

Molecular Biology Intelligence Unitic plasticity and changes in the network structure. Event driven computation optimizes processing speed in order to simulate networks with large number of neurons. The training procedure is applied to the face recognition task. Preliminary experiments on a public available face image dataset show th