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