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

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

This booklet 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 scholar symposium and the abstracts of three keynote displays have been rigorously reviewed and chosen from ninety submissions. The papers are prepared 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; computer studying and information mining; normal language processing; textual content analytics; reasoning and making plans; e-commerce; semantic net; laptop studying; and information mining.

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Additional 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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In: Proceedings of HLT-NAACL 2003 (2003) (short papers) 8. : Error-correcting output codes: A general method for improving multiclass inductive learning programs. In: The Ninth National Conference on Artificial Intelligence (AAAI 1991), pp. 572–577 (1991) 9. : Error-correcting output coding for text classification. In: IJCAI 1999 Workshop on Machine Learning for Information Filtering (1999) 10. : Improving Multiclass Text Classification with the Support Vector Machine. Massachusetts Institute of Technology, AI Memo, AIM-2001-026 (2001) 11.

Among these algorithms, Bayesian classifier achieved better results by reducing the classification error rates. The naive Bayesian classifier [6,11,14], along with many other classification algorithms, treat spam filtering as a binary classification problem, that is, the incoming email is either spam or non-spam. In reality, this simple treatment is too restrict and could result in losing vital information for users by misclassifying a legitimate email to spam. , a common word in email spam filter word list) in its header.

For the positive region, we have: P (C|x) ≥ α ⇐⇒ α λP N − λBN P r(C|x) ≥ = . P r(C c |x) 1−α λBP − λP P (19) According to equation (5), we can re-expressed the above equation as: n λP N − λBN P r(xi |C) P r(C) ≥ . 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.

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