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 complaints of the twenty fifth Canadian convention on synthetic Intelligence, Canadian AI 2012, held in Toronto, Canada, in may well 2012. The 23 general papers, sixteen brief papers, and four papers from the Graduate scholar Symposium awarded have been rigorously reviewed and chosen for inclusion during this publication. The papers hide a huge variety of subject matters offering unique paintings in all parts of man-made intelligence, both theoretical or applied.

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J. Mach. Learn. Res. ca Abstract. We present results of sentiment analysis in Twitter messages that disclose personal health information. In these messages (tweets), users discuss ailment, treatment, medications, etc. We use the authorcentric annotation model to label tweets as positive sentiments, negative sentiments or neutral. The results of the agreement among three raters are reported and discussed. We then use Machine Learning methods on multi-class and binary classification of sentiments.

To learn a fixed macro set, we use ParamILS in exactly the way it is used for parameter configuration. The input is a planner, a domain definition, an initial ordered set of macros, S, and a set of training problems. S is encoded as |S| binary parameters, each one indicating the presence or absence of a macro in the macro set. We represent a macro set as a vector of these indicator variables. For example, if |S| = 4, the parameter configuration (1, 0, 0, 1) indicates a macro set using only the first and fourth macros.

2709, pp. 146–155. Springer, Heidelberg (2003) 20. : The support vector machine under test. Neurocomputing 55(1-2), 169–186 (2003) 21. : Improved Pairwise Coupling Classification with Correcting Classifiers. , Rouveirol, C. ) ECML 1998. LNCS, vol. 1398, pp. 160–171. Springer, Heidelberg (1998) 22. : Efficient Pairwise Classification. , Skowron, A. ) ECML 2007. LNCS (LNAI), vol. 4701, pp. 658–665. Springer, Heidelberg (2007) 23. : Large margin dags for multiclass classification. Advances in Neural Information Processing Systems 12, 547–553 (2000) 24.

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