Read e-book online AI 2011: Advances in Artificial Intelligence: 24th PDF

By Mani Abedini, Michael Kirley (auth.), Dianhui Wang, Mark Reynolds (eds.)

ISBN-10: 364225831X

ISBN-13: 9783642258312

This e-book constitutes the refereed complaints of the twenty fourth Australasian Joint convention on man made Intelligence, AI 2011, held in Perth, Australia, in December 2011. The eighty two revised complete papers awarded have been conscientiously reviewed and chosen from 193 submissions. The papers are equipped in topical sections on information mining and information discovery, computer studying, evolutionary computation and optimization, clever agent structures, common sense and reasoning, imaginative and prescient and snap shots, picture processing, ordinary language processing, cognitive modeling and simulation know-how, and AI applications.

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Read or Download AI 2011: Advances in Artificial Intelligence: 24th Australasian Joint Conference, Perth, Australia, December 5-8, 2011. Proceedings PDF

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Extra resources for AI 2011: Advances in Artificial Intelligence: 24th Australasian Joint Conference, Perth, Australia, December 5-8, 2011. Proceedings

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O+N R. 22 Win/Draw/Loss vs Nominal 10/0/8 9/0/9 4/0/14 10/0/8 7/2/9 Win/Draw/Loss vs Random Ordinal 17/0/1 14/0/4 16/0/2 16/0/2 15/0/3 ◦ significant improvement against Nominal • significant degradation against Nominal 34 A. Berry and M. Cameron-Jones significantly outperformed. This makes it clear that under some circumstances an ordinal style split is of great importance to classification. The correct order method (S. Ord) outperformed the random method (R. Ord) 16 out of 18 times, across a wide variety of datasets; this is strong evidence that if an ordinal split is employed, correct ordinal information is vital to classification as opposed to just using random information.

One possibility for quantifying the quality of our method is to consider the family of classifiers inheriting the VQ mechanism. One such strategy that belongs to the supervised family is the LVQ1, while the SOM and the TTOSOM primarily learn the distributions using the unsupervised learning paradigm. The three classifiers utilized the same parameters, which are described in Section 4. Besides, while the LVQ1 and the SOM utilized 128 neurons, the results shown for the TTOSOM include only 15 neurons.

Imposing tree-based topologies onto self organizing maps. Information Sciences 181(18), 3798–3815 (2011) 2. : Modern Information Retrieval. , Boston (1999) 3. : Clustering unlabeled data with SOMs improves classification of labeled real-world data. In: Proc. of the 2002 International Joint Conference on Neural Networks, IJCNN 2002, vol. 3, pp. 2237–2242 (2002) 4. : Semi-supervised clustering using genetic algorithms. In: Artificial Neural Networks in Engineering (ANNIE 1999), pp. 809– 814 (1999) 5.

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AI 2011: Advances in Artificial Intelligence: 24th Australasian Joint Conference, Perth, Australia, December 5-8, 2011. Proceedings by Mani Abedini, Michael Kirley (auth.), Dianhui Wang, Mark Reynolds (eds.)


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