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 comprises 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 nations. The evaluate technique used to be held in levels. within the ?rst degree, the submissions have been assessed for his or her relevance and clarity via the Senior software Committee participants. these submissions that handed the ?rst degree have been then reviewed via no less than 3 software Committee individuals and self sufficient reviewers. After broad disc- sions, the Committee determined to simply accept 60 commonplace papers (acceptance expense of 31%) and forty four brief papers (acceptance fee of 22.7%). usual papers and 4 brief papers have been as a consequence withdrawn and aren't incorporated within the court cases. AI 2007 featured invited talks from 4 the world over exotic - 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 awarded in an interactive poster consultation and contributed to a st- ulating convention. It used to be an outstanding 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

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A. Orgun and J. ): AI 2007, LNAI 4830, pp. 16–25, 2007. c Springer-Verlag Berlin Heidelberg 2007 Efficient Learning of Bayesian Network Classifiers 17 between the attributes of the network in order to capture correlations among them. Such correlations are however restricted to a tree structure. Friedman et al showed that TAN was indeed more accurate than NB on benchmark datasets. Herein, we introduce a Bayesian network classifier less restrictive than NB and TAN classifiers. Considering that learning an unrestricted network is unfeasible, the underlying graph of the proposed classifier is confined to be consistent with the breadth-first search (BFS) order of an optimal TAN and to have a bounded in-degree, henceforward called BFS-consistent k-graph (BCkG).

The goal of learning a Bayesian network is to find both the structure and the parameters of the network that best fit the data, according to a given scoring function. The inference of a general Bayesian network has been shown to be an NP-hard problem [5], even for approximate solutions [7]. As a consequence, heuristic algorithms became the standard methodology for addressing this problem [23]. A common approach is to impose restrictions over the network structure. In this context, two results set the border between efficient and non-efficient structure learning.

The proposed classifier scores always better than both TAN and NB classifiers. Moreover, experiments on modeling transcription factor binding sites show that, in many cases, the improved scores translate into increased classification accuracy. f. Section 3 comment after Definition 1), (i) consider a random order over attributes at the same level or (ii) apply the TAN algorithm solely to attributes at the same level and order them with a BFS over the resulting TAN; combine and compare exhaustively our approach with other state-of-the-art Bayesian network learning methods; extending BCkG to deal with missing values and non discretized continuous variables; applying BCkG to a wider variety of datasets.

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