Download e-book for kindle: 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.)

ISBN-10: 3642378064

ISBN-13: 9783642378065

ISBN-10: 3642378072

ISBN-13: 9783642378072

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 contains 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: computer studying and development reputation; laptop imaginative and prescient and snapshot processing; robotics; wisdom illustration, reasoning, and scheduling; clinical purposes of man-made 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

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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. Also we should take into account that humor usually is not expressed directly. For example, let us consider the tweet ‘Mi novio es como mi iPhone. No tengo. (My boyfriend is like my iPhone. I don’t have one)’. It was automatically classified as positive, but the human annotator marked it as neutral. In fact, there is no enough information to guess correctly.

Formally, the algorithm has five steps: 1. Choose one centroid, c1 , at random from Dataset I. Initialise C = {c1 }. 2. Set D(x) as the shortest Euclidean distance from an entity i ∈ I to the closest centroid. 3. Take a new centroid, cn , with probability D(x)2 / i∈I D(x)2 . Add cn to C 4. If the cardinality of C is smaller than K, go back to Step 2. 5. Run K-Means, initialising it with all of the centroids in C. The algorithm is not deterministic; hence we ran it 100 times per dataset, as for the Random initialization.

We can observe this in our experiments with the Agglomerative Ward and Divisive Ward criterion, but due to poor scalability these may not be the best options when dealing with large datasets. It seems that iK-Means and the Hartigan and Wong algorithm are the most scalable of the algorithms we experimented with, but the latter seems unable to minimise the number of K-Means iterations. The Build algorithm was able to produce a substantial reduction in some experiments, but it proved to be the slowest initialization algorithm in the experiments.

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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 by Grigori Sidorov, Sabino Miranda-Jiménez, Francisco Viveros-Jiménez, Alexander Gelbukh (auth.), Ildar Batyrshin, Miguel González Mendoza (eds.)


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