By Aysha Akther, Heung-Nam Kim, Majdi Rawashdeh, Abdulmotaleb El Saddik (auth.), Leila Kosseim, Diana Inkpen (eds.)
This e-book constitutes the refereed complaints of the twenty fifth Canadian convention on man made Intelligence, Canadian AI 2012, held in Toronto, Canada, in may well 2012. The 23 typical papers, sixteen brief papers, and four papers from the Graduate scholar Symposium awarded have been conscientiously reviewed and chosen for inclusion during this e-book. The papers disguise a large diversity of subject matters offering unique paintings in all parts of synthetic intelligence, both theoretical or applied.
Read Online 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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Extra info for Advances in Artificial Intelligence: 25th Canadian Conference on Artificial Intelligence, Canadian AI 2012, Toronto, ON, Canada, May 28-30, 2012. Proceedings
5 Discussion and Analysis We set out to investigate the utility of recasting macro learning as parameter conﬁguration: conﬁguring parameters of the domain rather than of the solver. For learning ﬁxed macro sets, we showed that ParamILS-based macro learning performs as well or better than state-of-the-art macro learning tools: performing better in six domains while showing no signiﬁcant diﬀerence in ﬁve. In 10 of these 11 domains the macro set proposed by our system was as good as the best-onaverage macro set, representing a reasonable upper bound on the performance of any ﬁxed-macro set approach.
The process of merging the outputs of individual binary classiﬁers is usually called decoding. The most commonly decoding methods are the Hamming distance. This method looks for the minimum distance between the prediction vector and codewords. The ECOC method was then extended by Allwein et al.  using a coding matrix with three values, 1, 0, −1, where the zero value means that a given class is not considered in the training phase of a particular classiﬁer. In this way, a class can be omitted in the training of a particular binary classiﬁer.
In instance-speciﬁc learning, macro sets are chosen for each problem instance based on its features. Oﬄine, our approach clusters training instances based on their features. For each cluster, we then use parameter conﬁguration as in the ﬁxed-macro case to ﬁnd a strong macro set. The vector of feature measurements and the macro set for each cluster are then input to a standard machine learning algorithm to produce a predictor that maps instance features to macro sets. We found that this approach requires substantially less learning time and produces 24 M.
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.)