By Andrzej Bielecki, Marzena Bielecka, Anna Chmielowiec (auth.), Leszek Rutkowski, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada (eds.)
This ebook constitutes the refereed complaints of the ninth overseas convention on man made Intelligence and smooth Computing, ICAISC 2008, held in Zakopane, Poland, in June 2008.
The 116 revised contributed papers offered have been rigorously reviewed and chosen from 320 submissions. The papers are geared up in topical sections on neural networks and their purposes, fuzzy platforms and their functions, evolutionary algorithms and their functions, type, rule discovery and clustering, photograph research, speech and robotics, bioinformatics and scientific purposes, a number of difficulties of synthetic intelligence, and agent systems.
Read Online or Download Artificial Intelligence and Soft Computing – ICAISC 2008: 9th International Conference Zakopane, Poland, June 22-26, 2008 Proceedings PDF
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Additional info for Artificial Intelligence and Soft Computing – ICAISC 2008: 9th International Conference Zakopane, Poland, June 22-26, 2008 Proceedings
The distance between the neighboring receptors Δ in any direction determined by a ﬁxed angle ϕ is: Δ ≤ d/2. If the class of images represented by functions F (x, y) is given one may choose number of neurons and ampliﬁcations in such a way that all points of ridges and maxima are indicated and all disturbances are omitted. Adaptation algorithm and experiments. It results from (18) that it depends on value of p/a as well as discretization step whether the maximum is ”noticed” by the neural net. e Δx = Δy = Δ.
R − 1}, where r is the number of neurons in the original neuron chain or a given sub-chain. Condition 1: wini < β1 , where wini is the number of wins of i-th neuron and β1 is experimentally selected parameter (usually, for complex multidimensional WWW-document clustering, β1 assumes the value around 50). This condition allows to remove single neuron whose activity (measured by the number of its wins) is below an assumed level represented by parameter β1 . r−1 dj,j+1 , where di,i+1 is the distance between the Condition 2: di,i+1 > α1 j=1r neurons no.
In general, given a collection of WWW documents, the task of document clustering is to group documents together in such a way that the documents within each cluster are as ”similar” as possible to each other and as ”dissimilar” as possible from those of the other clusters. This paper presents a clustering technique based on dynamic self-organizing neural networks (introduced by the same authors in ,  and some earlier papers such as , ) and its application to a large-scale and highly multidimensional WWW-newsgroup-document clustering problem.