Download Artificial Neural Networks – ICANN 2009: 19th International by André Ventura, João M. Franco, João P. Ramos, Bruno Direito, PDF

By André Ventura, João M. Franco, João P. Ramos, Bruno Direito, António Dourado (auth.), Cesare Alippi, Marios Polycarpou, Christos Panayiotou, Georgios Ellinas (eds.)

This quantity set LNCS 5768 and LNCS 5769 constitutes the refereed lawsuits of the nineteenth foreign convention on synthetic Neural Networks, ICANN 2009, held in Limassol, Cyprus, in September 2009.

The two hundred revised complete papers offered have been rigorously reviewed and chosen from greater than three hundred submissions. the second one quantity is split in topical sections on neuroinformatics and bioinformatics; cognitive machines; facts research and development acceptance; sign and time sequence processing; neural dynamics and intricate platforms; imaginative and prescient and photograph processing; neuro-evolution and hybrid recommendations for cellular brokers keep watch over; neural regulate, making plans and robotics functions; clever instruments and techniques for multimedia annotation; and important infrastructure systems.

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Extra resources for Artificial Neural Networks – ICANN 2009: 19th International Conference, Limassol, Cyprus, September 14-17, 2009, Proceedings, Part II

Example text

2 Methodology In this paper we consider two general approaches to feature subset selection, more specifically, wrapper and filter approaches, for gene selection. Wrappers and filters differ in the evaluation of feature subsets. Filter approaches remove irrelevant features according to general characteristics of the data. Wrapper approaches, by contrast, apply machine learning algorithms to feature subsets and use cross-validation to evaluate the score of feature subsets. In theory, wrappers should provide more accurate classification results than filters [11].

1015 Lu´ıs A. Alexandre and Mark J. Embrechts Author Index . . . . . . . . . . . . . . . . . . . . . . . . . 1025 Epileptic Seizure Prediction and the Dimensionality Reduction Problem Andr´e Ventura, Jo˜ ao M. Franco, Jo˜ ao P. pt Abstract. Seizures prediction may substantially improve the quality of life of epileptic patients. Processing EEG signals, by extracting a convenient set of features, is the most promising way to classify the brain state and to predict with some antecedence its evolution to a seizure condition.

1015 Lu´ıs A. Alexandre and Mark J. Embrechts Author Index . . . . . . . . . . . . . . . . . . . . . . . . . 1025 Epileptic Seizure Prediction and the Dimensionality Reduction Problem Andr´e Ventura, Jo˜ ao M. Franco, Jo˜ ao P. pt Abstract. Seizures prediction may substantially improve the quality of life of epileptic patients. Processing EEG signals, by extracting a convenient set of features, is the most promising way to classify the brain state and to predict with some antecedence its evolution to a seizure condition.

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