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

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

This ebook contains the court cases of the overseas convention on man made Neural Networks (ICANN 2006) hung on September 10-14, 2006 in Athens, Greece, with tutorials being awarded on September 10, the most convention happening in the course of September 11-13 and accompanying workshops on belief, 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 foreign Neural community Society, the japanese Neural community Society and the IEEE Computational Intelligence Society. it's the foremost ecu occasion protecting all issues considering neural networks and similar parts. The ICANN sequence of meetings used to be initiated in 1991 and shortly turned the foremost eu collecting for specialists in those fields. In 2006 the ICANN convention was once prepared by means of the clever platforms Laboratory and the picture, Video and Multimedia structures Laboratory of the nationwide Technical collage of Athens in Athens, Greece. From 475 papers submitted to the convention, the foreign application Committee chosen, following a radical peer-review technique, 208 papers for book and presentation to 21 ordinary and 10 distinct periods. the standard of the papers got used to be mostly very excessive; consequently, it was once impossible to just accept and comprise within the convention application many papers of excellent quality.

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Additional info for Artificial Neural Networks – ICANN 2006: 16th International Conference, Athens, Greece, September 10-14, 2006. Proceedings, Part I

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A Functional Approach to Variable Selection in Spectrometric Problems 2 13 Functional Data Analysis The idea is this paper is based on the concepts of Functional Data Analysis (FDA [4]). The main idea of FDA is to adapt standard data analysis methods such that they make explicit use of the functional aspect of their inputs. There are two standard approaches in FDA: regularization and filtering. The regularization approach addresses the overfitting problem via complexity control. Let us consider for example the problem of linear regression on functional data.

There are many effective methods of feature extraction. One can consider here linear and nonlinear feature extraction procedures, particularly ones which ([4], [5]): 1. assume underying Gaussian distribution in the data ([6], [7], [8]), 2. utilize nonparametric sample-based methods when data cannot be described with the Gaussian model ([9]), 3. minimize the empirical probability of Bayes error ([6], [10]), 4. maximize the criteria for the information values of the individual features (or sets of features) describing the objects ([4], [5], [11]).

Sang-Jun Han, Sung-Bae Cho 884 Learning Manifolds in Forensic Data . . . . . . . . . . . . . . . . Frédéric Ratle, Anne-Laure Terrettaz-Zufferey, Mikhail Kanevski, Pierre Esseiva, Olivier Ribaux A Comparison of Target Customers in Asian Online Game Markets: Marketing Applications of a Two-Level SOM . . . . . . . . . . . . Sang-Chul Lee, Jae-Young Moon, Yung-Ho Suh 894 904 XXXIV Table of Contents – Part II Real World Applications (I) A Neural Network Approach to Study O3 and PM10 Concentration in Environmental Pollution .

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