Download PDF by Dekang Lin (auth.), Atefeh Farzindar, Vlado Kešelj (eds.): Advances in Artificial Intelligence: 23rd Canadian

By Dekang Lin (auth.), Atefeh Farzindar, Vlado Kešelj (eds.)

ISBN-10: 3642130593

ISBN-13: 9783642130595

This ebook constitutes the refereed court cases of the twenty third convention on man made Intelligence, Canadian AI 2010, held in Ottawa, Canada, in May/June 2010. The 22 revised complete papers awarded including 26 revised brief papers, 12 papers from the graduate pupil symposium and the abstracts of three keynote shows have been rigorously reviewed and chosen from ninety submissions. The papers are geared up in topical sections on textual content category; textual content summarization and IR; reasoning and e-commerce; probabilistic computing device studying; neural networks and swarm optimization; computing device studying and knowledge mining; normal language processing; textual content analytics; reasoning and making plans; e-commerce; semantic internet; laptop studying; and information mining.

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Read or Download Advances in Artificial Intelligence: 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 – June 2, 2010. Proceedings PDF

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In: Proceedings of the 13th International Joint Conference on Artificial Intelligence, pp. 1022–1029 (1993) 5. : Key concepts in model selection: performance and generalizability. Journal of Mathematical Psychology 44, 205–231 (2000) 6. : The Estimation of Probabilities: An Essay on Modern Bayesian Methods. MIT Press, Cambridge (1965) 7. : Do statistical inferences allowing three alternative decision give better feedback for environmentally precautionary decision-making. Journal of Environmental Management 85, 338–344 (2007) 8.

The first dataset is composed mostly small of sentences using abusive language, and the second one contains rather long sentences full of sarcasms and ironic phrases; therefore we decided to combine them together in order to see the performance over a generic and typical offensive language detection task, rather a specific category. H. Razavi et al. We used a total number of 1525 messages (1038 (68%) Okay and 487 (32%) Flame), from the two datasets together, from which 10% was used as a test set, and the rest was used as training set for our multi-level classifier.

Am } be a finite set of m possible actions. Let λ(ai |wj ) denote the loss, or cost, for taking action ai when the state is wj . Let P r(wj |x) be the conditional probability of an email being in state wj given that the email is described by x. For an email with description x, suppose action ai is taken. Since P r(wj |x) is the probability that the true state is wj given x, the expected loss associated with taking action ai is given by: s R(ai |x) = λ(ai |wj )P r(wj |x). (6) j=1 The quantity R(ai |x) is also called the conditional risk.

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Advances in Artificial Intelligence: 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 – June 2, 2010. Proceedings by Dekang Lin (auth.), Atefeh Farzindar, Vlado Kešelj (eds.)


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