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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 court cases of the twenty fifth Canadian convention on synthetic Intelligence, Canadian AI 2012, held in Toronto, Canada, in might 2012. The 23 standard 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 variety of subject matters offering unique paintings in all parts of man-made intelligence, both theoretical or applied.

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Extra 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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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].

However, in both these methods, the learning algorithm which finds the two most likely classes is the same as the final classification algorithm. Consequently, it is very likely that some classification errors will be common, arising from the limitation of base learner on certain patterns. A. Bagheri, Q. Gao, and S. Escalera the individual problems that are needed to train on are significantly larger. In addition, the problems are usually more difficult to learn in one-versus-all, as the classes have more overlapping instances.

For SVM Efficient Pairwise Classification Using Local Cross Off Strategy 31 Table 1. 1) developed by Chang and Lin [4]. For performance evaluation, we utilized 10-fold cross-validation to improve the reliability of the results. In order to have a fair comparison, the training and test sets of all methods were the same for each repetition of the experiments. 2 Experimental Results The average accuracy of the six methods as well as the original KNN classifier for the 20 datasets is presented in Table 2 and Table 3.

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