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 foreign convention on synthetic Intelligence, MICAI 2012, held in San Luis Potosí, Mexico, in October/November 2012. The eighty revised papers offered have been rigorously reviewed and chosen from 224 submissions. the 1st quantity comprises forty papers representing the present major subject matters of curiosity for the AI neighborhood and their functions. The papers are prepared within the following topical sections: laptop studying and trend acceptance; laptop imaginative and prescient and photo processing; robotics; wisdom illustration, reasoning, and scheduling; scientific purposes of synthetic 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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Additional info 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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The quality of the case base is important to every supervised classifier, and Nearest Neighbor (NN) is one of the most affected by it; because it stores the case base and compares every new case with those stored, having a time and memory costs increasing with the dimensions of the case base. There are several methods to improve NN classifiers through simultaneous or combined feature and instance selection, having some drawbacks such as a stochastic nature, high computational I. Batyrshin and M.

IK-Means. The intelligent K-Means algorithm (iK-Means) introduced by Mirkin [4], finds initial centroids for K-Means based on the concept of anomalous patterns (AP). The algorithm iteratively finds the centroids by picking the entity most distant from the dataset centre and applying K-Means with two initial centroids: the found entity and the data centre. After convergence, the cluster initiated with the found entity is removed from the dataset and the process restarts until all of the data is clustered.

The similarity relations generate similarity classes, for each object x∈U. The recently introduced Minimum Neighborhood Rough Sets [8] defines the similarity relation using Maximum Similarity Graph concepts. Two objects are similar (neighbors) if they form an arc in a Maximum Similarity Graph, that is, the Neighborhood of an object is ( ) , ∈ . Let be Y ∈ Y a decision class, its positive region is as following: (Y ) ∈ , ( ), ∈ (d) (d) i (1) Therefore, objects with pure neighborhood will form the positive region of the decision classes.

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