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Advances in Artificial Intelligence: 25th Canadian by Aysha Akther, Heung-Nam Kim, Majdi Rawashdeh, Abdulmotaleb

By Aysha Akther, Heung-Nam Kim, Majdi Rawashdeh, Abdulmotaleb El Saddik (auth.), Leila Kosseim, Diana Inkpen (eds.)

This booklet constitutes the refereed court cases of the twenty fifth Canadian convention on man made Intelligence, Canadian AI 2012, held in Toronto, Canada, in might 2012. The 23 average papers, sixteen brief papers, and four papers from the Graduate pupil Symposium offered have been conscientiously reviewed and chosen for inclusion during this ebook. The papers disguise a large diversity of themes providing unique paintings in all parts of man-made intelligence, both theoretical or applied.

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The relations, however, were studied on a small number of texts written by few authors. In the current study, we analyze the relations on a massive amount of text contributed by many authors. 1 It has > 200 mln. 2 A user can post publicly visible messages ≤ 140 characters, often with shortenings: On my way c [see] vicki Shes recovering frm [from] surgery. Other users can subscribe to these tweets and respond with their messages. com/topsites L. Kosseim and D. ): Canadian AI 2012, LNAI 7310, pp.

The next decision was which base classifier to use. In this study, two base learners were chosen: Support Vector Machines (SVMs) using linear kernel and a Multilayer Perceptron (MLP). The SVM, MLP, and KNN classifiers cannot handle the missing values, so the instances with missing values were removed. For the MLP neural network, we chose 10 hidden nodes and the hyperbolic tangent transfer function and for linear SVM, we set C = 10. The experiments were all implemented in MATLAB software. For SVM Efficient Pairwise Classification Using Local Cross Off Strategy 31 Table 1.

Gao, and S. Escalera Experimental Result Analysis As can be seen in Tables 2-5, the proposed approach is generally able to outperform all the other methods for the two types of base learners. As a general conclusion, the advanced performance of the LCO method does not differ much depending on the base classifier. 06% for MLP and linear SVM, respectively. The other finding is that the one-versus-all method generally performs poorly in the present experiments, especially for MLP neural network. An analysis of the results shows a somewhat clearer picture.

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