By Patrick Doherty, Piotr Rudol (auth.), Mehmet A. Orgun, John Thornton (eds.)
This quantity comprises the papers awarded 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 method was once held in phases. within the ?rst degree, the submissions have been assessed for his or her relevance and clarity by means of the Senior application Committee contributors. these submissions that handed the ?rst level have been then reviewed by means of at the least 3 software Committee participants and self reliant reviewers. After huge disc- sions, the Committee made up our minds to simply accept 60 standard papers (acceptance price of 31%) and forty four brief papers (acceptance expense of 22.7%). common papers and 4 brief papers have been thus withdrawn and aren't integrated within the lawsuits. AI 2007 featured invited talks from 4 the world over unusual - 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 favored. 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 an excellent excitement for us to function this system Co-chairs of AI 2007.
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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Extra resources for AI 2007: Advances in Artificial Intelligence: 20th Australian Joint Conference on Artificial Intelligence, Gold Coast, Australia, December 2-6, 2007. Proceedings
These steps are iterated as necessary and are analogous to the Expectation (E) and Maximization (M) steps of the EM algorithm. In implementation, a basic strategy is to separate the dependence between the hidden variables (Z, β, ρ), that is, to assume that Q(Z, β, ρ) = Q(Z)Q(β)Q(ρ). In this case, substituting (6) into the right hand side of (7) results in F (Q, Θ) = H(Q(Z)) + H(Q(β)) + H(Q(ρ)) + β,ρ Z N m zij log πj − i=1 j=1 − 1 2 1 d log(2π) + 2 2 d log ρjk + k=1 d 2 ρjk βijk (yik − μjk )2 − log 2βijk − k=1 m Q(Z)Q(β)Q(ρ) 1 2 d log βijk k=1 1 2βijk (8) d ajk log(bjk ) + (ajk − 1) log ρjk − bjk ρjk − log Γ (ajk ) dβdρ + j=1 k=1 where Q is any density function with respect to the corresponding random variables and H(Q) is the entropy of the Q.
The data are shown in each plot of Figure 1 in which the noised points are marked as +. Ideally we hope the extra 50 data won’t do much impact on the model as the majority data come from a Gaussian mixture model. However it is clear from Figure 1(a) that the standard Gaussian mixture model attempts to model the noised data2 . However the L1 mixture model has easily revealed the true component patterns without much impact from the noised data, see Figure 1(b). In the experiment, we also noted that the β value associated with the extra outliers is signiﬁcantly smaller than the β values for the other points.
ICML 2004, pp. 46–53. ACM Press, New York (2004) 17. : Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning 20(3), 197–243 (1995) 18. : Learning Bayesian belief networks: An approach based on the MDL principle. Comp. Intell. 10, 269–294 (1994) 19. : Non-additivity in protein-DNA binding. Bioinformatics 21(10), 2254–2263 (2005) 20. : A feature-based approach to modeling protein-DNA interactions. In: Proc. RECOMB 2007, LNCS(LNBI), vol. 4453, pp. 77–91. Springer, Heidelberg (2007) 21.
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.)