Read e-book online 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 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 international locations. The overview approach was once held in levels. within the ?rst degree, the submissions have been assessed for his or her relevance and clarity through the Senior application Committee contributors. these submissions that handed the ?rst degree have been then reviewed by way of at the least 3 application Committee individuals and self reliant reviewers. After large disc- sions, the Committee made up our minds to just accept 60 usual papers (acceptance cost of 31%) and forty four brief papers (acceptance fee of 22.7%). ordinary papers and 4 brief papers have been thus withdrawn and aren't integrated within the complaints. AI 2007 featured invited talks from 4 the world over amazing - 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 liked. 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 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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The data set consists of 200 data, 150 of which were sampled from a three-component Gaussian mixture model, and other 50 data from a uniform distribution over the 120% range determined by the first 150 data points. 5 with mixing coefficients π1 = π2 = π3 = 1/3. 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 .

No analytical forms available for p(Y |Θ). If we wish to proceed, we need to turn to an approximate method. We are going to look at the variational Bayesian inference method. 3 Variational Approximation for L1 Mixture Model In order to introduce the variational learning method, the following notation is used: Let (Z, β, ρ) be the model latent variables, Θ is the hyperparameters in (6). For the given observation Y , the ML algorithm aims to maximize the log likelihood: L(Θ) = log p(Y |Θ) = log Z β,ρ p(Y, Z, β, ρ|Θ)dβdρ Using any distribution Q(Z, β, ρ) over the latent variables, called variational distributions, we can obtain a lower bound on L: L(Θ) = log Z ≥ Z β β ρ ρ p(Y, Z, β, ρ|Θ)dβdρ Q(Z, β, ρ) log p(Y, Z, β, ρ|Θ) dβdρ Q(Z, β, ρ) (7) Denote by F (Q(Z, β, ρ), Θ) the right hand side of the above inequality.

T R with the highest score. Moreover, this graph is acyclic since the parents of a node Xi must be in αi , that is, must belong to the path in R from its root to Xi (excluding Xi ). Moreover, it is easy to see that for any path Xi1 , Xi2 , . . Xik in G we have that Xij ∈ αik for 1 ≤ j < k. If there existed a cycle Xi1 , Xi2 , . . Xi1 it would imply that Xi1 ∈ αi1 which is absurd. Proposition 1. Algorithm 1 constructs a BCkG Bayesian network classifier whose φ-score is always greater than, or equal to, the φ-score of the optimal TAN.

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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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