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 court cases of the eleventh Mexican foreign convention on man made 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 comprises forty papers representing the present major subject matters of curiosity for the AI neighborhood and their purposes. The papers are prepared within the following topical sections: computing device studying and trend acceptance; laptop imaginative and prescient and picture processing; robotics; wisdom illustration, reasoning, and scheduling; clinical functions of synthetic 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
Villuendas-Rey, Y. M. García-Lorenzo cost, insufficient noise filtering, and inability to deal with imbalanced case bases. This paper introduces a novel deterministic method to improve NN classifier by selecting features and instances, which makes this process better than other combined methods. The main contributions of the proposal are: 1. It has strong theoretic basis, because it uses extended Rough Set Theory  and structuralizations of the Logical Combinatorial Approach to Pattern Recognition .
The human annotator assigns here usually a negative opinion because he supposes that the display does not work anymore. However, this supposition is based on beliefs of the annotator, and not on the real situation. The SVM classifier assigned here the neutral value, because there is insufficient information for other type of decision. 2 Misspelling Orthographic errors are common in tweets such as ‘No me gushta le iPhone’ ‘Ya tng iPhone de nuevo (I don’t like the iPhone; I have an iPhone again)’.
2. For each cluster in S, set its centroid equal to 1 + (k − 1) ∗ [N/K]th entity. Hierarchical Agglomerative Ward. Ward’s hierarchical method  allows the creation of a dendrogram describing the whole dataset. Milligan  suggests using it to determine the initial centroids for K-Means. The method can be formalised as: 1. 2. 3. 4. Set every entity as the centroid of its own cluster. Merge the closest clusters Sw1 and Sw2 , following the Ward distance. Remove the references to Sw1 and Sw2 , leaving only the new cluster, Sw1∪w2 .