Get AI 2007: Advances in Artificial Intelligence: 20th PDF

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

ISBN-10: 3540769269

ISBN-13: 9783540769262

This quantity includes the papers provided 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 nations. The assessment approach was once held in phases. within the ?rst degree, the submissions have been assessed for his or her relevance and clarity through the Senior software Committee contributors. these submissions that handed the ?rst level have been then reviewed through no less than 3 software Committee individuals and self sustaining reviewers. After vast disc- sions, the Committee determined to just accept 60 commonplace papers (acceptance cost of 31%) and forty four brief papers (acceptance fee of 22.7%). usual papers and 4 brief papers have been for this reason withdrawn and aren't incorporated within the court cases. AI 2007 featured invited talks from 4 across the world exclusive - searchers, particularly, 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 preferred. AI 2007 additionally featured workshops on integrating AI and data-mining, semantic biomedicine and ontology. the quick papers have been offered in an interactive poster consultation and contributed to a st- ulating convention. It was once a good excitement for us to function this system Co-chairs of AI 2007.

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Extra 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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We denote the joint domain D = Πi=1 Di . The second component G = (N, E) is a directed acyclic graph with nodes N = {X1 , . . , Xn } and edges E representing direct dependencies between the variables. The third component Θ encodes the parameters {θxi |Πxi }x∈D of the network, where θxi |Πxi = PB (xi |Πxi ) for each possible value xi of Xi , and Πxi of ΠXi , where ΠXi denotes the set of parents of Xi in G. M. L. -F. Sagot n PB (X1 , . . , Xn ) = θXi |ΠXi . (1) i=1 We denote the set of all Bayesian networks with n variables by Bn .

M. L. -F. Sagot BFS-Consistent Bayesian Network Classifiers We now introduce the main contribution of this paper, a simple and effective heuristic for a causality order between the attributes based on a breadth-first search (BFS) over an optimal TAN. The main idea is to take the total order induced by the BFS over an optimal TAN and then search for an optimal network (of bounded in-degree) consistent with it. It is easy to show that the score of the resulting network is always greater than or equal to the score of TAN and NB.

Learning polytrees. In: Proc. UAI 1999, pp. 134–141 (1999) 1 Friedmann et al also noticed that the learning algorithm for Bayesian classifiers should maximize the conditional likelihood scoring function (CLL) instead of the likelihood. However, CLL is not decomposable, and therefore learning it seems to be intractable. Efficient Learning of Bayesian Network Classifiers 25 9. : Beyond independence: Conditions for the optimality of the simple Bayesian classifier. In: Proc. ICML1996, pp. 105–112 (1996) 10.

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AI 2007: Advances in Artificial Intelligence: 20th Australian Joint Conference on Artificial Intelligence, Gold Coast, Australia, December 2-6, 2007. Proceedings by Patrick Doherty, Piotr Rudol (auth.), Mehmet A. Orgun, John Thornton (eds.)


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