Read e-book online Advances in Artificial Intelligence: 21st Conference of the PDF

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 reports of Intelligence, Canadian AI 2008, held in Windsor, Canada, in may well 2008.

The 30 revised complete papers awarded including five revised brief papers have been rigorously reviewed and chosen from seventy five submissions. The papers current unique high quality examine in all components of man-made Intelligence and observe historic AI strategies to trendy challenge domain names in addition to fresh suggestions to ancient challenge settings.

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Read or Download 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 PDF

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Extra resources 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

We then decided to use the very popular SVM-Light package [17] to further examine the benefits of using an SVM approach. 2 was used to obtain feature vectors for each of the blogs in the test set and the results were converted into WEKA and SVM-Light formats. A number of popular words, or N , was first tested to determine the optimal number of features to utilize for this classification task. Table 2 shows the results of testing a varying number of features in SVMLight. From the results, it is fairly clear to see that the utilization of a small number of features such as 50 is ineffective.

For a theory Γ , we denote by Atoms(Γ ) the set of atomic formulas that occur in Γ . The set of valuations for L is Λ = { p1 : a1 , . . , pm : am | a1 , . . , am ∈ {t, f }}. The set of models of a formula ψ is a subset of Λ, defined as follows: mod(pi ) = { p1 : a1 , . . , pi : t, . . , pm : am | a1 , . . , ai−1 , ai+1 , . . , am ∈ {t, f }}, mod(¬ψ) = Λ \ mod(ψ), mod(ψ ∧ ϕ) = mod(ψ) ∩ mod(ϕ), mod(ψ ∨ ϕ) = mod(ψ) ∪ mod(ϕ). For a theory Γ = {ψ1 , . . , ψn } we define mod(Γ ) = mod(ψ1 ) ∩ . .

Ai−1 , ai+1 , . . , am ∈ {t, f }}, mod(¬ψ) = Λ \ mod(ψ), mod(ψ ∧ ϕ) = mod(ψ) ∩ mod(ϕ), mod(ψ ∨ ϕ) = mod(ψ) ∪ mod(ϕ). For a theory Γ = {ψ1 , . . , ψn } we define mod(Γ ) = mod(ψ1 ) ∩ . . ∩ mod(ψn ). For defining distance-based entailments we recall the definitions in [1,2]. Definition 1. A pseudo-distance on U is a total function d : U× U → N so that for all ν, μ ∈ U d(ν, μ) = d(μ, ν) (symmetry), and d(ν, μ) = 0 iff ν = μ (identity preservation). A distance (metric) d on U is a pseudo-distance that satisfies the triangular inequality: for all ν, μ, σ ∈ U , d(ν, σ) ≤ d(ν, μ) + d(μ, σ).

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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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