Download Advances in Artificial Intelligence: 11th Mexican by Grigori Sidorov, Sabino Miranda-Jiménez, Francisco PDF

By Grigori Sidorov, Sabino Miranda-Jiménez, Francisco Viveros-Jiménez, Alexander Gelbukh (auth.), Ildar Batyrshin, Miguel González Mendoza (eds.)

The two-volume set LNAI 7629 and LNAI 7630 constitutes the refereed lawsuits of the eleventh Mexican overseas convention on synthetic Intelligence, MICAI 2012, held in San Luis Potosí, Mexico, in October/November 2012. The eighty revised papers awarded have been rigorously reviewed and chosen from 224 submissions. the 1st quantity contains forty papers representing the present major themes of curiosity for the AI neighborhood and their purposes. The papers are geared up within the following topical sections: desktop studying and trend reputation; computing device imaginative and prescient and photograph processing; robotics; wisdom illustration, reasoning, and scheduling; clinical functions of man-made intelligence.

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Extra resources for Advances in Artificial Intelligence: 11th Mexican International Conference on Artificial Intelligence, MICAI 2012, San Luis Potosí, Mexico, October 27 – November 4, 2012. Revised Selected Papers, Part I

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Opinion Identification in Spanish Texts. In: Proceedings of the NAACL HLT (2010) 10. 1: Five Years of Open-Source Language Processing Tools. In: Proceedings of 7th Language Resources and Evaluation Conference, La Valletta, Malta (2010) 11. : Yahoo! For Amazon: Extracting market sentiment from stock message boards. In: Proceedings of the 8th Asia Pacific Finance Association Annual Conference (2001) 12. : The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1) (2009) 13. EAGLES. Recommendations for the morphosyntactic annotation of corpora, Eag-tcwgmac/r, ILC-CNR, Pisa (1996) 14.

W. 81 22 R. Cordeiro de Amorim In this initial set of experiments, we were surprised by the good performance of the divisive ward algorithm in regard to the number of iterations. This kept the number of K-Means iterations from two to three and was outperformed only once by iK-Means, by a single iteration in the wine dataset(possibly the most well-separated of them). The agglomerative ward algorithm also presented a good average outcome, always providing a lower number of iterations than those obtained by random initialization.

Compact sets guaranteed that the most similar example of each instance belong to the same compact of the instance. It is usual to construct a MSG using , 1 ∆( , ), where ∆ , is a dissimilarity function. Maximum Similarity Graphs are the basis for several prototype selection methods, such as [4-6], and offers several advantages to data analysis. They do not assume any properties of data and do not need any parameter for their construction, except the similarity function to compare two instances.

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