Download e-book for kindle: Advances in Artificial Intelligence: 23rd Canadian by Dekang Lin (auth.), Atefeh Farzindar, Vlado Kešelj (eds.)

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

ISBN-10: 3642130593

ISBN-13: 9783642130595

This ebook constitutes the refereed complaints of the twenty third convention on synthetic 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 pupil symposium and the abstracts of three keynote shows have been conscientiously reviewed and chosen from ninety submissions. The papers are prepared in topical sections on textual content class; textual content summarization and IR; reasoning and e-commerce; probabilistic computing device studying; neural networks and swarm optimization; computing device studying and knowledge mining; ordinary language processing; textual content analytics; reasoning and making plans; e-commerce; semantic internet; computer studying; and information mining.

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Extra info for Advances in Artificial Intelligence: 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 – June 2, 2010. Proceedings

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Rough set based decision model in information retrieval and filtering. In: Third World Multiconference on Systemics, Cybernetics and Informatics (SCI 1999) and Fifth International Conference on Information Systems Analysis and Synthesis (ISAS 1999), vol. 5, pp. 398–403 (1999) 9. : Classifying news stories using memory based reasoning. In: Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 59–65 (1992) 10. html 11. : Machine Learning.

C ) P r(C c ) P r(x |C λBP − λP P i i=1 (20) This computation can be further simplified by taking the logarithm of both side of equation (20): n log i=1 P r(C c ) λP N − λBN P r(xi |C) ≥ log + log . P r(xi |C c ) P r(C) λBP − λP P (21) c ) Here log PPr(C is independent of the description of emails, we treat it as a r(C) constant. Similar expression can be obtained for the negative region as: n log i=1 P r(C c ) λBN − λN N P r(xi |C) ≤ log + log . c P r(xi |C ) P r(C) λN P − λBP (22) A new pair of threshold values α and β can be defined as: λP N − λBN P r(C c ) + log , P r(C) λBP − λP P λBN − λN N P r(C c ) β = log + log , P r(C) λN P − λBP α = log (23) where P r(C)/P r(C c ) can be easily estimated from the frequencies of the training data by putting: P r(C) = |C| |C c | and P r(C c ) = .

Following the discussions given in the book by Duda and Hart [3], the basic ideas of the theory are reviewed. Let Ω = {w1 , . . , ws } be a finite set of s states and let A = {a1 , . . , 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).

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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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