Advances in Artificial Intelligence: 23rd Canadian - download pdf or read online

By Atefeh Farzindar, Vlado Keselj

ISBN-10: 3642130585

ISBN-13: 9783642130588

This e-book constitutes the refereed lawsuits of the twenty third convention on synthetic Intelligence, Canadian AI 2010, held in Ottawa, Canada, in May/June 2010. The 22 revised complete papers offered including 26 revised brief papers, 12 papers from the graduate pupil symposium and the abstracts of three keynote displays have been rigorously reviewed and chosen from ninety submissions. The papers are geared up 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; laptop studying and information mining; average language processing; textual content analytics; reasoning and making plans; e-commerce; semantic net; computing device studying; and knowledge mining.

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Extra resources for Advances in Artificial Intelligence: 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 - June 2, 2010,

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In: Proc. Third International ICWSM Conference, pp. 278–281 (2009) 5. : Emotions from text: machine learning for text- based emotion prediction. In: Proc. Joint Conference on Human Language Technology / Empirical Methods in Natural Language Processing (HLT/EMNLP 2005), Vancouver, Canada, pp. 579–586 (2005) 6. : SemEval-2007 Task 14: Affective Text (2007) 7. : Learning to Identify Emotions in Text. In: Proc. ACM Symposium on Applied computing, Fortaleza, Brazil, pp. 1556–1560 (2008) 8. : Upar7: A knowledge-based system for headline sentiment tagging.

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. In our approach, 32 B. Zhou, Y. Yao, and J. Luo a pair of threshold values is used to make a three-way decision of an incoming email.

Consequently, we use SVM as a machine-learning algorithm to be applied to the chosen features. 3% for the last feature set. Based on the accuracy of the three experiments, unigram features outperform the other two types. Even though it is significantly better than the third one, its difference with the second feature set merits a discussion. Here, we would like to point out parameters – other than the accuracy of the results – which should be considered in choosing the list of features. The number of features is one of the main parameters.

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Advances in Artificial Intelligence: 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 - June 2, 2010, by Atefeh Farzindar, Vlado Keselj

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