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By Rocío Alaiz-Rodríguez (auth.), Sabine Bergler (eds.)

ISBN-10: 3540688218

ISBN-13: 9783540688211

ISBN-10: 3540688250

ISBN-13: 9783540688259

This ebook constitutes the refereed complaints of the twenty first convention of the Canadian Society for Computational experiences of Intelligence, Canadian AI 2008, held in Windsor, Canada, in may perhaps 2008.

The 30 revised complete papers provided including five revised brief papers have been rigorously reviewed and chosen from seventy five submissions. The papers current unique top quality study in all parts of man-made Intelligence and practice ancient AI ideas to fashionable challenge domain names in addition to fresh options to historic challenge settings.

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Extra info for Advances in Artificial Intelligence: 21st Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2008 Windsor, Canada, May 28-30, 2008 Proceedings

Example text

Given these results, it appears that the selection of words that are included in the dictionary is very important for the lexical approach. If the dictionary is too sparse or exhaustive, one risks the chance of over or under analyzing the results, leading to a decrease in performance. In accordance with previous findings, our results confirm that it is difficult to surpass the 65% accuracy level using a purely lexical approach. 3 Machine Learning Results During the initial experimentation phase of the machine learning approach, we used the WEKA package [16] to obtain a general idea of which ML algorithms and methods would be best suited to classify the dataset we had.

Immediate from Proposition 5. Corollary 4. For every theory Γ , the procedure MPV(GU , Γ ) terminates after at most maxdU Λ iterations and computes ΔdU,max (Γ ). Proof. By Propositions 3 and 6. Another way of computing consequences of the entailment relation |=dU ,max is by the deduction system Su max , defined in Figure 2. This system manipulates expressions of the form Γ : V , where Γ is a theory and V ⊆ Λ. – Axioms: ∅:Λ (A0 ) {ψ} : mod(ψ) if mod(ψ) = ∅ (A1 ) {ψ} : Λ if mod(ψ) = ∅ (A2 ) – Inference Rules: Γ1 : V 1 Γ2 : V 2 if mod(Γ1 ∪ Γ2 ) = ∅ (I1 ) Γ1 ∪ Γ2 : V 1 ∩ V 2 Γ1 : V 1 Γ2 : V 2 if mod(Γ1 ∪ Γ2 ) = ∅ Γ1 ∪ Γ2 : Λ (I2 ) Fig.

The proportion of positive and negative token matches did not change, and we hypothesize that for each positive match that was found, it was equally likely to find a negative match, thereby creating a neutral net effect. Given these results, it appears that the selection of words that are included in the dictionary is very important for the lexical approach. If the dictionary is too sparse or exhaustive, one risks the chance of over or under analyzing the results, leading to a decrease in performance.

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Advances in Artificial Intelligence: 21st Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2008 Windsor, Canada, May 28-30, 2008 Proceedings by Rocío Alaiz-Rodríguez (auth.), Sabine Bergler (eds.)


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