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 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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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 [12]. 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 [9]. ParamILS is a general-purpose algorithm tuner designed to deal with a very large space of numerical and categorical parameters [9].

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