New PDF release: Advances in Artificial Intelligence: 25th Canadian

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

ISBN-10: 3642303536

ISBN-13: 9783642303531

This ebook constitutes the refereed complaints of the twenty fifth Canadian convention on man made Intelligence, Canadian AI 2012, held in Toronto, Canada, in may perhaps 2012. The 23 usual papers, sixteen brief papers, and four papers from the Graduate pupil Symposium offered have been conscientiously reviewed and chosen for inclusion during this booklet. The papers conceal a large diversity of subject matters proposing unique paintings in all components of synthetic intelligence, both theoretical or applied.

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Extra resources for Advances in Artificial Intelligence: 25th Canadian Conference on Artificial Intelligence, Canadian AI 2012, Toronto, ON, Canada, May 28-30, 2012. Proceedings

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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. 37–48, 2012. c Springer-Verlag Berlin Heidelberg 2012 38 V. Bobicev et al. , tweets) present a real-time means of estimating public interest in various subjects, including personal health and expressed sentiments.

The main idea of [9] is to include information about classes not included in each pairwise partition by using the confusion in the training data. However, performance improvement is not always guarantee, and the computationally complexity of the method is slightly Efficient Pairwise Classification Using Local Cross Off Strategy 29 increased. Some works have also used the idea of irrelevant classifiers to prune classifiers in ensemble schemes based on the low confidence output of binary classifiers [25].

At the decoding step, the outputs of the individual binary classifiers are computed for a test pattern, and the sample is classified by the class with the code at minimum distance given a particular decoding measurement. Among the three approaches, one-versus-one and one-versus-all are the two most commonly used, mainly because of their clarity in comparison with the ECOC approach. However, both the one-versus-one and one-versus-all methods have their drawbacks. In the one-versus-all method, each binary classifier is trained on an unbalanced training set,f tending to produce a negative output for all classifiers [5].

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Advances in Artificial Intelligence: 25th Canadian Conference on Artificial Intelligence, Canadian AI 2012, Toronto, ON, Canada, May 28-30, 2012. Proceedings by Aysha Akther, Heung-Nam Kim, Majdi Rawashdeh, Abdulmotaleb El Saddik (auth.), Leila Kosseim, Diana Inkpen (eds.)


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