By Patrick Doherty, Piotr Rudol (auth.), Mehmet A. Orgun, John Thornton (eds.)
This quantity comprises 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 evaluate procedure used to be held in phases. within the ?rst degree, the submissions have been assessed for his or her relevance and clarity via the Senior software Committee individuals. these submissions that handed the ?rst degree have been then reviewed by way of no less than 3 software Committee contributors and self sufficient reviewers. After vast disc- sions, the Committee made up our minds to just accept 60 average papers (acceptance cost of 31%) and forty four brief papers (acceptance fee of 22.7%). typical papers and 4 brief papers have been for that reason withdrawn and aren't integrated within the complaints. AI 2007 featured invited talks from 4 across the world distinctive - 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 vastly preferred. AI 2007 additionally featured workshops on integrating AI and data-mining, semantic biomedicine and ontology. the quick papers have been awarded in an interactive poster consultation and contributed to a st- ulating convention. It used to be a very good excitement for us to function this system Co-chairs of AI 2007.
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Extra resources for AI 2007: Advances in Artificial Intelligence: 20th Australian Joint Conference on Artificial Intelligence, Gold Coast, Australia, December 2-6, 2007. Proceedings
RECOMB 2007, LNCS(LNBI), vol. 4453, pp. 77–91. Springer, Heidelberg (2007) 21. : Full Bayesian network classiﬁers. In: Proc. ICML 2006, pp. 897–904. ACM Press, New York (2006) 22. : A construction of Bayesian networks from databases based on an MDL principle. In: Proc. UAI 1993, pp. 266–273 (1993) 23. : Ordering-based search: A simple and eﬀective algorithm for learning Bayesian networks. In: Proc. UAI 2005, pp. 584–591 (2005) Mixture of the Robust L1 Distributions and Its Applications Junbin Gao and Richard Y.
Proceedings of the International Workshop on Integrating AI and Data Mining, pp. 11–17. IEEE Computer Society Press, Los Alamitos (2006) On Using a Hierarchy of Twofold Resource Allocation Automata to Solve Stochastic Nonlinear Resource Allocation Problems Ole-Christoﬀer Granmo1 and B. John Oommen2, 2 1 Dept. of ICT, University of Agder, Grimstad, Norway School of Computer Science, Carleton University, Ottawa, Canada Abstract. Recent trends in AI attempt to solve diﬃcult NP-hard problems using intelligent techniques so as to obtain approximately-optimal solutions.
F is referred to as the negative free energy. When Q(Z, β, ρ) is equal to the true joint posterior of (Z, β, ρ) given the data set Y , viz. p(Z, β, ρ|Y, Θ), then F (Q, Θ) = L(Θ). The diﬀerence between this lower bound F (Q, Θ) and L(Θ) is the KL-divergence between the true and approximating posteriors. The Bayesian 30 J. Y. Xu variational learning is to maximise F with respect to the distribution Q and the parameters θ alternatively. These steps are iterated as necessary and are analogous to the Expectation (E) and Maximization (M) steps of the EM algorithm.