Artificial Neural Networks – ICANN 2006: 16th International by Nojun Kwak, Chunghoon Kim (auth.), Stefanos D. Kollias,

By Nojun Kwak, Chunghoon Kim (auth.), Stefanos D. Kollias, Andreas Stafylopatis, Włodzisław Duch, Erkki Oja (eds.)

This publication comprises the court cases of the overseas convention on synthetic Neural Networks (ICANN 2006) hung on September 10-14, 2006 in Athens, Greece, with tutorials being provided on September 10, the most convention occurring in the course of September 11-13 and accompanying workshops on notion, cognition and interplay hung on September 14, 2006. The ICANN convention is equipped each year through the ecu Neural community Society in cooperation with the overseas Neural community Society, the japanese Neural community Society and the IEEE Computational Intelligence Society. it's the top-rated ecu occasion protecting all themes all in favour of neural networks and comparable parts. The ICANN sequence of meetings was once initiated in 1991 and shortly grew to become the most important ecu accumulating for specialists in those fields. In 2006 the ICANN convention was once prepared via the clever structures Laboratory and the picture, Video and Multimedia structures Laboratory of the nationwide Technical college of Athens in Athens, Greece. From 475 papers submitted to the convention, the overseas application Committee chosen, following a radical peer-review procedure, 208 papers for book and presentation to 21 ordinary and 10 targeted periods. the standard of the papers bought used to be usually very excessive; thus, it was once impossible to just accept and comprise within the convention application many papers of excellent quality.

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O. kr Abstract. In manipulating data such as in supervised learning, we often extract new features from the original features for the purpose of reducing the dimensions of feature space and achieving better performance. In this paper, we show how standard algorithms for independent component analysis (ICA) can be applied to extract features for regression problems. The advantage is that general ICA algorithms become available to a task of feature extraction for regression problems by maximizing the joint mutual information between target variable and new features.

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