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 synthetic Intelligence, Canadian AI 2012, held in Toronto, Canada, in might 2012. The 23 average papers, sixteen brief papers, and four papers from the Graduate scholar Symposium awarded have been conscientiously reviewed and chosen for inclusion during this publication. The papers disguise a vast diversity of subject matters proposing unique paintings in all parts of synthetic intelligence, both theoretical or applied.
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Additional resources for Advances in Artificial Intelligence: 25th Canadian Conference on Artificial Intelligence, Canadian AI 2012, Toronto, ON, Canada, May 28-30, 2012. Proceedings
5. FF and macros from Wizard’s chunking phase. FF and macros from Macro-FF CA-ED. FF and manually constructed macros. VHPOP and macros from Macro-FF CA-ED. VHPOP and manually constructed macros. Table 3. 1: Average run-times (in seconds) of the domain models constructed from chunking phase of Wizard using the FF planners. 05. 65 Macro Learning as Parameter Conﬁguration 21 Our interest here is to test whether our ParamILS-based instance-speciﬁc macro set (DIR-P) is as strong as the exhaustive instance-speciﬁc macro set (DIR), while requiring signiﬁcantly less oﬄine computation.
As we mentioned before, the methods for multiclass problems can be generally categorized into three approaches: One-Versus-All(OVA): The one-versus-all method constructs c binary classiﬁers, one for each class. The ith classiﬁer, hi , is trained with data from class i as positive instances and all data from the other classes as negative instances. A new instance is classiﬁed in the class whose corresponding classiﬁer output has the largest value. One-Versus-One (OVO): The one-vs-one method, also called pairwise classiﬁcation, constructs c(c − 1)/2 classiﬁers .
This literature proves that if each classiﬁer makes diﬀerent errors, then the total errors can be reduced by an appropriate combination of these classiﬁers. Due to the instability of neural networks, they are suitable candidates to be used in the ECOC approach. However, the ECOC approach is more computationally expensive than the LCO method. On the other hand, stable classiﬁers such as support vector machines cannot take advantage of the ECOC approach. Consequently, the results of the ECOC method are even worse than that of one-versus-one and LCO.