Artificial Neural Networks - ICANN 2008: 18th International by Anton Andrejko, Mária Bieliková (auth.), Véra Kůrková, Roman

By Anton Andrejko, Mária Bieliková (auth.), Véra Kůrková, Roman Neruda, Jan Koutník (eds.)

This quantity set LNCS 5163 and LNCS 5164 constitutes the refereed lawsuits of the 18th overseas convention on synthetic Neural Networks, ICANN 2008, held in Prague Czech Republic, in September 2008.

The two hundred revised complete papers provided have been conscientiously reviewed and chosen from greater than three hundred submissions. the second one quantity is dedicated to development reputation and information research, and embedded platforms, computational neuroscience, connectionistic cognitive technological know-how, neuroinformatics and neural dynamics. it additionally includes papers from exact classes coupling, synchronies, and firing styles: from cognition to affliction, and optimistic neural networks and workshops new tendencies in self-organization and optimization of synthetic neural networks, and adaptive mechanisms of the perception-action cycle.

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Extra info for Artificial Neural Networks - ICANN 2008: 18th International Conference, Prague, Czech Republic, September 3-6, 2008, Proceedings, Part II

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Therefore, we introduce weights to personalize enumeration what allows computing similarity taking into account user’s individuality. Now, similarity is evaluated as follows: sim (InstA, InstB ) = |A∩B| i=0 weighti × GeneralSMi (SetA, SetB ) weight (2) where the assigned meaning of variables is the same as in Eq. 1. The variable weight is computed for each attribute that two instances have in common. It gets a value from range 1, w according to the match with corresponding characteristic in the user model.

G. by cross-validation) returns a biased estimate of the relevance of the subset itself. Secondly, we propose a low-bias estimator of the relevance based on the cross-validation assessment of an unbiased learner. Third, we assess a feature selection approach which combines the low-bias relevance estimator with state-of-the-art relevance estimators in order to enhance their accuracy. The experimental validation on 20 publicly available cancer expression datasets shows the robustness of a selection approach which is not biased by a specific learner.

IEEE Transactions on Pattern Analysis and Machine Intelligence (2005) 10. : On the use of variable complementarity for feature selection in cancer classification. , Takagi, H. ) EvoWorkshops 2006. LNCS, vol. 3907, pp. 91–102. Springer, Heidelberg (2006) 11. : Bias plus variance decomposition for zero-one loss functions. In: Prooceedings of the 13th International Conference on Machine Learning, pp. 275–283 (1996) 12. : A Probabilistic Theory of Pattern Recognition. Springer, Heidelberg (1996) 13.

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