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 overseas 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 functions. The papers are equipped within the following topical sections: desktop studying and development popularity; desktop imaginative and prescient and photo processing; robotics; wisdom illustration, reasoning, and scheduling; scientific functions of synthetic intelligence.

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Extra 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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Remove any cluster whose cardinality is below a user-specified threshold. 6. Using the found centroids, apply K-Means. In our experiments, we were not interested in finding the quantity of clusters in the datasets, which is a complex problem on its own; hence when applying iK-Means, we set its threshold to zero and chose the K largest clusters, where K is the given number of clusters. Where two clusters had the same cardinality, preference was given to the one that was found first, as this was the the most anomalous.

C Springer-Verlag Berlin Heidelberg 2013 16 R. Cordeiro de Amorim this algorithm is not faultless; its flaws include the lack of a universally agreed definition for the term ’cluster’ [18], whose number the algorithm assumes to be known beforehand. This raises a complex question: how can one quantify something that is undefined? Plausible answers are subjectively related to the data being clustered. K-Means is a greedy algorithm; as such, its outcome is highly dependent on its initial centroids, which are normally chosen at random.

Misspelled words and shortness of the messages make difficult for a classifier to determine the right class. The human annotator marked the first one as negative, and the second one as positive, but the SVM classifier assigned neutral class in both cases. 3 Humor, Irony and Sarcasm Treatment of humor and its subtypes like irony or sarcasm is an interesting but extremely difficult problem. It is very complex because while interpreting humor we often rely on the world knowledge and the (very broad) context, as well as on much more difficult to represent subtle cultural patterns.

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