Download AI 2007: Advances in Artificial Intelligence: 20th by Patrick Doherty, Piotr Rudol (auth.), Mehmet A. Orgun, John PDF

By Patrick Doherty, Piotr Rudol (auth.), Mehmet A. Orgun, John Thornton (eds.)

This quantity includes the papers offered at AI 2007: the twentieth Australian Joint convention on Arti?cial Intelligence held in the course of December 2–6, 2007 at the Gold Coast, Queensland, Australia. AI 2007 attracted 194 submissions (full papers) from 34 international locations. The evaluate method was once held in phases. within the ?rst level, the submissions have been assessed for his or her relevance and clarity by means of the Senior software Committee contributors. these submissions that handed the ?rst level have been then reviewed via a minimum of 3 software Committee individuals and self sufficient reviewers. After broad disc- sions, the Committee made up our minds to just accept 60 general papers (acceptance price of 31%) and forty four brief papers (acceptance cost of 22.7%). common papers and 4 brief papers have been to that end withdrawn and aren't incorporated within the lawsuits. AI 2007 featured invited talks from 4 the world over wonderful - searchers, specifically, Patrick Doherty, Norman Foo, Richard Hartley and Robert Hecht-Nielsen. They shared their insights and paintings with us and their contri- tions to AI 2007 have been tremendously favored. AI 2007 additionally featured workshops on integrating AI and data-mining, semantic biomedicine and ontology. the fast papers have been provided in an interactive poster consultation and contributed to a st- ulating convention. It used to be a good excitement for us to function this system Co-chairs of AI 2007.

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Read or Download AI 2007: Advances in Artificial Intelligence: 20th Australian Joint Conference on Artificial Intelligence, Gold Coast, Australia, December 2-6, 2007. Proceedings PDF

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Additional info for AI 2007: Advances in Artificial Intelligence: 20th Australian Joint Conference on Artificial Intelligence, Gold Coast, Australia, December 2-6, 2007. Proceedings

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Due to the heavy tail characteristics of the L1 distribution, the proposed model is more robust against data outliers. In this paper, we generalized the L1-PCA into a mixture of L1-distributions so that the model can be used for possible multiclustering data. For the model learning we use the property that the L1 density can be expanded as a superposition of infinite number of Gaussian densities to include a tractable Bayesian learning and inference based on the variational EMtype algorithm. 1 Introduction In many applications, multivariate observations have frequently been represented using a mixture of Gaussian models [2].

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Run a (deterministic) algorithm that outputs an optimal TAN R according to φ. 2. Compute the total order induced by the BFS over R (ignoring the class variable). 3. For each attribute Xi in R do: (a) Compute the set αi = {Xj ∈ R : Xi Xj and Xi = Xj }. (b) For each subset S of αi with at most k attributes: i. Compute φi (S ∪ {C}, T ). ii. If φi (S ∪ {C}, T ) is the maximal score for Xi , set Πi = S ∪ {C}. 4. Output the directed graph G such that the parents of an attribute Xi are Πi . Theorem 1 (Soundness).

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