Artificial Neural Networks and Neural Information Processing by Gürsel Serpen PhD (auth.), Okyay Kaynak, Ethem Alpaydin,

By Gürsel Serpen PhD (auth.), Okyay Kaynak, Ethem Alpaydin, Erkki Oja, Lei Xu (eds.)

This publication constitutes the refereed lawsuits of the joint overseas convention on man made Neural Networks and foreign convention on Neural details Processing, ICANN/ICONIP 2003, held in Istanbul, Turkey, in June 2003.

The 138 revised complete papers have been conscientiously reviewed and chosen from 346 submissions. The papers are geared up in topical sections on studying algorithms, aid vector desktop and kernel equipment, statistical info research, trend attractiveness, imaginative and prescient, speech popularity, robotics and keep an eye on, sign processing, time-series prediction, clever structures, neural community undefined, cognitive technological know-how, computational neuroscience, context acutely aware platforms, complex-valued neural networks, emotion popularity, and purposes in bioinformatics.

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Additional resources for Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003: Joint International Conference ICANN/ICONIP 2003 Istanbul, Turkey, June 26–29, 2003 Proceedings

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Starting from discussions on the basic properties of the f-divergence, we derive a new class of ICA algorithms called the f-ICA by minimizing this information measure. Contribution of this paper can be previewed as follows. (i) New properties of the f-divergence and related information measures are presented. (ii) The f-ICA contains usual logarithmic ICA as a special case. Convergence speed is faster than the logarithmic one. (iii) Obtained algorithms are modifiable to be partially supervised learning.

Figure 2 is the extracted activation pattern (a time course) which corresponds to an assigned on-off task to a subject (a young male)3 . , u1 . 4 was so successful. 064 -400 -500 0 20 40 60 80 100 120 140 160 180 200 Fig. 1. Learning speed. 062 0 10 20 30 40 50 60 70 80 Fig. 2. Corresponding activation. Fig. 3. Separation of V1 and V2. is the resulting brain map. This map clearly separates the edges of visual regions V1 and V2. Experiments were executable by a conventional personal computer. This is due to the increased speed of the presented algorithm which exploits the second term of Equation (18).

We apply this locality principle to the design of Simple Synchrony Networks (SSNs) [3,4] for estimating the probabilities of parser decisions. The resulting statistical parsers achieve performance roughly equivalent to the state-of-the-art. Performance with part-ofspeech tags as input is better than any other such parser. 6% below the best current parser, despite using a relatively small vocabulary. 2 Inducing History Representations with SSNs Natural language parsing takes a string of words of unbounded length (the sentence) and produces a tree structure of unbounded size (the parse).

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