By Aysha Akther, Heung-Nam Kim, Majdi Rawashdeh, Abdulmotaleb El Saddik (auth.), Leila Kosseim, Diana Inkpen (eds.)
This booklet constitutes the refereed court cases of the twenty fifth Canadian convention on man made Intelligence, Canadian AI 2012, held in Toronto, Canada, in may possibly 2012. The 23 standard papers, sixteen brief papers, and four papers from the Graduate scholar Symposium awarded have been rigorously reviewed and chosen for inclusion during this booklet. The papers hide a extensive diversity of subject matters proposing unique paintings in all parts of synthetic intelligence, both theoretical or applied.
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Extra info for Advances in Artificial Intelligence: 25th Canadian Conference on Artificial Intelligence, Canadian AI 2012, Toronto, ON, Canada, May 28-30, 2012. Proceedings
151–163. Springer, Heidelberg (1991) 7. : Statistical comparisons of classiﬁers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006) 8. : Solving multiclass learning problems via error-correcting output codes. Journal of Artiﬁcial Intelligence Research 2, 263–286 (1995) 9. : Re-coding ecocs without re-training. Pattern Recognition Letters 31, 555–562 (2010) 10. : Binary tree of svm: A new fast multiclass training and cassiﬁcation algorithm. IEEE Transactions on Neural Networks 17(696-704) (2006) 11.
According to the recommendations of Demsar , we consider the use of non-parametric tests. Non-parametric tests are safer than parametric tests, such as ANOVA and ttest, since they do not assume normal distribution or homogeneity of variance. A. Bagheri, Q. Gao, and S. Escalera In this study, we employ the Iman-Davenport test. If there are statistically signiﬁcant diﬀerences in the classiﬁcation performance, then we can proceed with the Nemenyi test as a post hoc test, which is used to compare six methods with each other.
On each of these domains, DIR-P was also signiﬁcantly better than BOA, supporting our hypothesis. – In VHPOP-MANUAL, there was one planning domain (blocksworld) where DIR was signiﬁcantly better than BOA, albeit by a small margin. DIR-P was not signiﬁcantly diﬀerent from BOA on this domain, failing to support our hypothesis. If we compared DIR with DIR-P directly, of the 23 macro source/planning/ domain combinations, DIR and DIR-P are statistically indistinguishable in 19 cases, and DIR is signiﬁcantly better in four cases, all using VHPOP-MANUAL.
Advances in Artificial Intelligence: 25th Canadian Conference on Artificial Intelligence, Canadian AI 2012, Toronto, ON, Canada, May 28-30, 2012. Proceedings by Aysha Akther, Heung-Nam Kim, Majdi Rawashdeh, Abdulmotaleb El Saddik (auth.), Leila Kosseim, Diana Inkpen (eds.)