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Titlebook: Advances in Artificial Intelligence - IBERAMIA 2002; 8th Ibero-American C Francisco J. Garijo,José C. Riquelme,Miguel Toro Conference proce

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發(fā)表于 2025-3-21 17:18:04 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Advances in Artificial Intelligence - IBERAMIA 2002
期刊簡稱8th Ibero-American C
影響因子2023Francisco J. Garijo,José C. Riquelme,Miguel Toro
視頻videohttp://file.papertrans.cn/147/146769/146769.mp4
發(fā)行地址Includes supplementary material:
學(xué)科分類Lecture Notes in Computer Science
圖書封面Titlebook: Advances in Artificial Intelligence - IBERAMIA 2002; 8th Ibero-American C Francisco J. Garijo,José C. Riquelme,Miguel Toro Conference proce
影響因子The 8th Ibero-American Conference on Artificial Intelligence, IBERAMIA 2002, took place in Spain for the second time in 14 years; the first conference was organized in Barcelona in January 1988. The city of Seville hosted this 8th conference, giving the participants the opportunity of enjoying the richness of its historical and cultural atmosphere. Looking back over these 14 years, key aspects of the conference, such as its structure, organization, the quantity and quality of submissions, the publication policy, and the number of attendants, have significantly changed. Some data taken from IBERAMIA’88 and IBERAMIA 2002 may help to illustrate these changes. IBERAMIA’88 was planned as an initiative of three Ibero-American AI associations: the Spanish Association for AI (AEPIA), the Mexican Association for AI (SMIA), and the Portuguese Association for AI (APIA). The conference was organized by the AEPIA staff, including the AEPIA president, José Cuena, the secretary, Felisa Verdejo, and other members of the AEPIA board. The proceedings of IBERAMIA’88 contain 22 full papers grouped into six areas: knowledge representation and reasoning, learning, AI tools, expert systems, language, and
Pindex Conference proceedings 2002
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978-3-540-00131-7Springer-Verlag Berlin Heidelberg 2002
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Francisco J. Garijo,José C. Riquelme,Miguel ToroIncludes supplementary material:
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https://doi.org/10.1007/978-94-011-0966-6s based on the not always realistic assumption that class-conditional distributions can be factorized in the product of their marginal densities. On the other side, one of the most common ways of estimating the Independent Component Analysis (ICA) representation for a given random vector consists in
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Molecular Genetics of Flower Senescence “good value” for .. To reduce the bias of . and take account of the different roles or influences that features play with respect to the decision attribute, we propose a novel asymmetric neighbourhood selection and support aggregation method in this paper. Our aim is to create a classifier less bia
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J. M. Van Tuyl,K. B. Lim,M. S. Ramannaegmented in the classes land (L), sea (S), fog (F), low clouds (C.), middle clouds (C.), high clouds (C.) and clouds with vertical growth (C.). The classification is performed from an initial set of several statistical textural features based on the gray level co-occurrence matrix (GLCM) proposed by
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M. Lorito,G. Del Sorbo,F. Scalaor noisy. This type of examples makes the Machine Learning Systems produce not adequate rules. In this paper we present an algorithm that filters noisy continuous labeled examples, whose computational cost is ..) for . examples and . attributes. Besides, it is shown experimentally to be better than
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