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Titlebook: MICAI 2007: Advances in Artificial Intelligence; 6th Mexican Internat Alexander Gelbukh,Ángel Fernando Kuri Morales Conference proceedings

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书目名称MICAI 2007: Advances in Artificial Intelligence
副标题6th Mexican Internat
编辑Alexander Gelbukh,Ángel Fernando Kuri Morales
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: MICAI 2007: Advances in Artificial Intelligence; 6th Mexican Internat Alexander Gelbukh,Ángel Fernando Kuri Morales Conference proceedings
描述Artificial Intelligence is a branch of computer science that studies heuristic methods of solving complex problems. Historically the first such tasks modeled human intellectual activity: reasoning, learning, seeing and speaking. Later similar methods were extended to super-complex optimization problems that appear in science, social life and industry. Many methods of Artificial Intelligence are borrowed from nature, where there occur similar super-complex problems such as those related to survival, development, and behavior of living organisms. The Mexican International Conference on Artificial Intelligence (MICAI), a yearly international conference series organized by the Mexican Society for Artificial Intelligence (SMIA), is a major international AI forum and the main event in the academic life of the country’s growing AI community. The proceedings of the previous MICAI events were published by Springer in its Lecture Notes in Artificial Intelligence (LNAI) series, vol. 1793, 2313, 2972, 3789, and 4293. Since its foundation in 2000, the conference has shown a stable growth in popularity (see Figures 1 and 3) and improvement in quality (see Fig. 2). The 25% acceptance rate milesto
出版日期Conference proceedings 2007
关键词artificial intelligence; bioinformatics; computational intelligence; computer vision; data mining; image
版次1
doihttps://doi.org/10.1007/978-3-540-76631-5
isbn_softcover978-3-540-76630-8
isbn_ebook978-3-540-76631-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2007
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Radial Basis Function Neural Network Based on Order Statisticsk is less biased by the presence of outliers in the training set and was proved an accurate estimation of the implied probabilities. From simulation results we show that the proposed neural network has better classification capabilities in comparison with other RBF based algorithms.
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Complete Recall on Alpha-Beta Heteroassociative Memoryhm based on the Alpha-Beta Heteroassociative memories that allows, besides correct recall of some altered patterns, perfect recall of all the trained patterns, without ambiguity. The theoretical support and some experimental results are presented.
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A New Global Optimization Algorithm Inspired by Parliamentary Political Competitionstions in trying to take the control of the parliament. Performance of this method for function optimization over some benchmark multi-dimensional functions, of which global and local minimums are known, is compared with traditional genetic algorithms.
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