Download Advances in Artificial Intelligence: 25th Canadian by Aysha Akther, Heung-Nam Kim, Majdi Rawashdeh, Abdulmotaleb PDF

By Aysha Akther, Heung-Nam Kim, Majdi Rawashdeh, Abdulmotaleb El Saddik (auth.), Leila Kosseim, Diana Inkpen (eds.)

This ebook constitutes the refereed complaints of the twenty fifth Canadian convention on synthetic Intelligence, Canadian AI 2012, held in Toronto, Canada, in could 2012. The 23 commonplace papers, sixteen brief papers, and four papers from the Graduate scholar Symposium provided have been conscientiously reviewed and chosen for inclusion during this publication. The papers hide a huge diversity of issues proposing unique paintings in all components of man-made intelligence, both theoretical or applied.

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Read or Download Advances in Artificial Intelligence: 25th Canadian Conference on Artificial Intelligence, Canadian AI 2012, Toronto, ON, Canada, May 28-30, 2012. Proceedings PDF

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Additional resources for Advances in Artificial Intelligence: 25th Canadian Conference on Artificial Intelligence, Canadian AI 2012, Toronto, ON, Canada, May 28-30, 2012. Proceedings

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Skowron, A. ) ECML 2007. LNCS (LNAI), vol. 4701, pp. 658–665. Springer, Heidelberg (2007) 23. : Large margin dags for multiclass classification. Advances in Neural Information Processing Systems 12, 547–553 (2000) 24. : Empirical analysis of support vector machine ensemble classifiers. Expert Systems with Applications 36(3, Part 2), 6466–6476 (2009) 25. : Binary labelling and decision-level fusion. Information Fusion 2(2), 103–112 (2001) 26. : Probability estimates for multi-class classification by pairwise coupling.

The main idea of [9] is to include information about classes not included in each pairwise partition by using the confusion in the training data. However, performance improvement is not always guarantee, and the computationally complexity of the method is slightly Efficient Pairwise Classification Using Local Cross Off Strategy 29 increased. Some works have also used the idea of irrelevant classifiers to prune classifiers in ensemble schemes based on the low confidence output of binary classifiers [25].

A. Bagheri, Q. Gao, and S. Escalera 16. : Combining predictions in pairwise classication: An optimal adaptive voting strategy and its relation to weighted voting. Pattern Recognition 43(1), 128–142 (2010) 17. : Approximations of the critical regions of the friedman statistic. Communications in Statistics 6, 571–595 (1980) 18. : Single-layer learning revisited: a stepwise procedure for building and training a neural network. In: Fogelman, J. ) Neurocomputing: Algorithms, Architectures and Applications.

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