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 overseas convention on man made Intelligence, MICAI 2012, held in San Luis Potosí, Mexico, in October/November 2012. The eighty revised papers provided have been conscientiously reviewed and chosen from 224 submissions. the 1st quantity contains forty papers representing the present major issues of curiosity for the AI group and their purposes. The papers are prepared within the following topical sections: laptop studying and development acceptance; laptop imaginative and prescient and photo processing; robotics; wisdom illustration, reasoning, and scheduling; scientific purposes of synthetic intelligence.
Read or Download 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 PDF
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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
This is a considerably larger real-world dataset than the other six discussed here, comprising 19,020 entities over 11 features partitioned into two clusters. The results for this dataset are contained in Table 7. Table 7. The outcome of the experiments with the MAGIC Gamma Telescope dataset; 19,020 entities over 11 features partitioned into two clusters. The non-deterministic initializations (Random and K-Means++) were run 100 times. Initializations Random K-Means++ iK-Means Build (PAM) H&W Agg.
1 Effect of the Corpus Size We tested different corpus sizes for training the three classifiers. In Table 2, we show how the corpus size affects precision. The precision was improved when using more training samples. Table 2. Precision observed when using 12 different training corpus sizes Classifier Part I. Corpus size (tweets) 1,000 1,500 2,000 2,500 3,000 3,500 4,000 Naïve Bayes 42 46 42 42 45 44 45 J48 43 49 46 49 50 52 54 SVM 48 55 55 59 61 59 60 Classifier Part II. Corpus size (tweets) 4,500 5,000 5,500 6,000 6,500 7,000 Naïve Bayes 42 43 44 43 44 46 J48 53 55 52 54 54 57 SVM 59 60 59 60 61 61 8 G.
2 Effect of n-Gram Size We perform tests to study the effect of the n-gram order (size) on the precision of classifiers. We tried six different sizes. Results are shown in Table 3. It confirms that unigram is the best feature size. This conclusion confirms the conclusions obtained in other studies for English language and different corpus domain such as Twitter and films reviews [4, 9]. Table 3. 3 N-gram size 1 2 3 4 5 6 Naïve Bayes 46 37 35 35 35 35 J48 57 41 35 35 35 35 SVM 61 49 41 35 35 35 Effect of the Number of Classes Table 4 describes the values of the number of classes and their composition.
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