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 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: computing device studying and trend reputation; computing device imaginative and prescient and photo processing; robotics; wisdom illustration, reasoning, and scheduling; scientific purposes of man-made intelligence.

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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

The two-volume set LNAI 7629 and LNAI 7630 constitutes the refereed complaints 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.

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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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There are other possible stop conditions [11,4]. In order to implement the above, we need an algorithm to perform the splitting of step 2. One of Mirkin’s suggestions is the 2-Means splitting procedure[11,4], as follows: 1. Given Sw , find the two entities with the maximum distance to be the initial centroids cw1 and cw2 . 2. Apply the K-Means algorithm to Sw , utilising cw1 and cw2 as the initial centroids. 3. Output Sw1 and Sw2 . An Empirical Evaluation of Different Initializations 3 19 Datasets and Experiments This paper presents experiments with 37 datasets, of which seven are real and 30 are synthetic.

Possible combinations for classifying classes Number of the classes Values in the classes Clustering of the values 2 positive, negative I. positive: positive, neutral, or news II. negative 3 positive, negative, neutral I. positive II. negative III. neutral: neutral or news positive, negative, neutral, news I. positive II. negative III. neutral IV. news 4 Empirical Study of Machine Learning Based Approach for Opinion Mining in Tweets 9 Table 5. 0 Table 5 shows the effect of the number of classes on the classifier performance.

1. Pick the entity most distant from the data centre, cd , as a tentative centroid ct . 2. Apply K-Means, using cd and ct as initial centroids. 3. After convergence, remove the cluster St from the dataset; cd remains unchanged. 4. If not all data is clustered, go to Step 1. 5. 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.

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