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Download e-book for iPad: Advances in Artificial Intelligence: 23rd Canadian by Atefeh Farzindar, Vlado Keselj

By Atefeh Farzindar, Vlado Keselj

This publication constitutes the refereed complaints 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 provided including 26 revised brief papers, 12 papers from the graduate scholar symposium and the abstracts of three keynote displays have been conscientiously 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 desktop studying; neural networks and swarm optimization; computing device studying and information mining; ordinary language processing; textual content analytics; reasoning and making plans; e-commerce; semantic net; computer studying; and knowledge mining.

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Farzindar, and G. Lapalme reason is that sometimes it is convenient to remove an unnecessary part of a sentence in a summary2 or to merge two short sentences. So when matching sentences, we have to look for part of sentences and decide whether it is relevant to identify sentences that have been shortened. We also found cases in which sentences or parts of them have been rewritten. This may cause misspelled words, case changes and even translations. Thus, our similarity function has to be tolerant to slight modifications.

If τ (x) is chosen so that R(τ (x)|x) is as small as possible for every x, the overall risk R is minimized. Thus, the optimal Bayesian decision procedure can be formally stated as follows. For every x, compute the conditional risk R(ai |x) for i = 1, . . , m defined by equation (6) and select the action for which the conditional risk is minimum. If more than one action minimizes R(ai |x), a tie-breaking criterion can be used. 4 A Three-Way Decision Approach to Email Spam Filtering In the naive Bayesian spam filter, an incoming email is classified as legitimate if the posterior odds ratio exceeds a certain threshold value.

Machine Learning. McGraw-Hill, New York (1997) 12. : The threshold approach to clinical decision making. New England Journal of Medicine (1980) 13. : Rough membership functions. , Kacprzyk, J. ) Advances in the Dempster-Shafer Theory of Evidence, pp. 251–271. John Wiley and Sons, New York (1994) 14. : A Bayesian approach to filtering junk e-mail. In: AAAI Workshop on Learning for Text Categorization, Madison, Wisconsin. AAAI Technical Report WS-98-05 (1998) 15. : BoosTexter: A boosting-based system for text categorization.

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