Advances in Artificial Intelligence: 11th Mexican - download pdf or read online

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

ISBN-10: 3642378064

ISBN-13: 9783642378065

ISBN-10: 3642378072

ISBN-13: 9783642378072

The two-volume set LNAI 7629 and LNAI 7630 constitutes the refereed complaints 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 subject matters of curiosity for the AI group and their functions. The papers are equipped within the following topical sections: desktop studying and trend acceptance; machine imaginative and prescient and photo processing; robotics; wisdom illustration, reasoning, and scheduling; scientific purposes of man-made intelligence.

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Read or Download 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 PDF

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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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Initializations Random K-Means++ iK-Means Build (PAM) H&W Agg. W. Div. W. 0385 The Pima Indian diabetes dataset consists of 768 entities, eight numerical features and two clusters. In terms of cardinality, the clusters have 500 and 268 entities. An update recently (28/02/2011) posted on the web page regarding this dataset [10], stating that some of its values are biologically implausible; however, An Empirical Evaluation of Different Initializations 21 we decided to keep the dataset unchanged for easy comparison with previously published papers.

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. Nonetheless, it was outperformed by iK-Means in five of the seven datasets. Regarding the initializations’ overhead time, the worst case was presented by the Build algorithm; it seems clear that the number of entities in a dataset can have a drastic impact on the amount of time it takes to complete.

In case of imbalanced dataa, if the minority class has overlapping with another class, error – based editing may delete d the entire minority class. When dealing with welll-separated classes, condensing methods obtain very goood results, and error – based editing do not offer a good instance reduction. On the otther Intelligent Feature and Insttance Selection to Improve Nearest Neighbor Classifiers 31 hand, when classes have a certain degree of overlapping, and are balanced, erroor based editing obtain very go ood performance.

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


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