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

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 conscientiously 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: laptop studying and development attractiveness; computing device imaginative and prescient and picture processing; robotics; wisdom illustration, reasoning, and scheduling; clinical purposes 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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Algorithm AS 136: A K-Means Clustering Algorithm. Journal of the Royal Statistical Society 28(1), 100–108 (1979) 10. Irvine machine learning repository (accessed September 05, 2011) 11. : Mathematical classification and clustering. Kluwer Academic Press, Dordrecht (1996) 12. : Finding groups in data: An introduction to cluster analysis. J. Wiley and Son (1990) 13. : A systematic evaluation of different methods for initializing the K-means clustering algorithm. Transactions on Knowledge and Data Engineering (2010) 14.

8% for positive and negative classes with the unbalanced corpus. 2% reported for English Twitter in [7]. It is interesting to observe that the precision decreased when using a balanced corpus, though not very much. 63). Adjectives and adverbs usually have more sentiment connotations. This phenomenon is part of our future research. Another interesting point is that the Decision Tree classifier (J48) in general is more stable as far as the effects of balancing of the corpus are concerned. Table 6.

Order all N entities in the dataset in respect to the gravity centre. 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 [7] allows the creation of a dendrogram describing the whole dataset. Milligan [14] 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.

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