By Aysha Akther, Heung-Nam Kim, Majdi Rawashdeh, Abdulmotaleb El Saddik (auth.), Leila Kosseim, Diana Inkpen (eds.)
This publication 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 ordinary papers, sixteen brief papers, and four papers from the Graduate scholar Symposium offered have been rigorously reviewed and chosen for inclusion during this ebook. The papers hide a huge diversity of themes offering unique paintings in all parts of synthetic intelligence, both theoretical or applied.
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Additional info for Advances in Artificial Intelligence: 25th Canadian Conference on Artificial Intelligence, Canadian AI 2012, Toronto, ON, Canada, May 28-30, 2012. Proceedings
In: ICAPS 2009 Workshop on Planning and Learning (2009) 2. : Improvement strategies for the f-race algorithm: Sampling design and iterative reﬁnement. Hybrid Metaheuristics, 108– 122 (2007) 3. : Macro-FF: Improving AI Planning with Automatically Learned Macro-Operators. Journal of Artiﬁcial Intelligence Research 24, 581–621 (2005) 4. : Empirical Methods for Artiﬁcial Intelligence. The MIT Press, Cambridge (1995) 5. : Marvin: A heuristic search planner with online macro-action learning. Journal of Artiﬁcial Intelligence Research 28(1), 119–156 (2007) 6.
V2 works signiﬁcantly better than one-versusone, one-versus-all, and A&O methods. Comparing the results of the LCO and ECOC methods, however, we can see that the classiﬁcation accuracy of the LCO method tends to be slightly better than that of both ECOC methods, but does not signiﬁcantly outperform ECOC methods. These results are consistent with the observations from Pedrajas and Boyer’s paper . Their results show that the performance of the ECOC method using a neural network as a base learner will signiﬁcantly increase as the number of classiﬁers increases.
While such parametrization signiﬁcantly adds to the solver’s customizability, the right parameter values must be found to maximize performance. Automated parameter tuning has made significant strides over the past few years and is often able to ﬁnd parameters that achieve performance substantially better than can be achieved by an expert or, indeed, the original designer of the solver . ParamILS is a general-purpose algorithm tuner designed to deal with a very large space of numerical and categorical parameters .