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 publication constitutes the refereed court cases of the twenty fourth Australasian Joint convention on synthetic Intelligence, AI 2011, held in Perth, Australia, in December 2011. The eighty two revised complete papers provided have been rigorously reviewed and chosen from 193 submissions. The papers are geared up in topical sections on info mining and data discovery, computing device studying, evolutionary computation and optimization, clever agent structures, common sense and reasoning, imaginative and prescient and images, photo processing, typical language processing, cognitive modeling and simulation know-how, and AI applications.

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

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

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Ordinal attributes are both discrete and ordered. An example is T-shirt sizes (S, M, L, XL): they are logically discrete but also have an implied order, and if you are considering which one to buy then knowledge of that order is potentially useful. A decision tree can use such order information by treating an ordinal attribute as a numeric attribute, and searching for binary splits of the values rather than splitting the tree one way for each different value [12]. This may be advantageous because by comparison with the one branch per value case, during the training process more test cases are available at each of the lower nodes to use in decision making, possibly increasing accuracy.

This certainly is an avenue for further research. 6 Conclusions The purpose of this paper was to design and present an experimental analysis of a novel PR scheme based on the TTOSOM. Our classifier combined the information provided by labeled and unlabeled instances simultaneously. Our experimental results showed that the TTOSOM classifier possesses an improved classification accuracy in comparison to other VQ-based classifiers. A. J. Oommen state-of-the-art schemes, even when the number of neurons utilized is only a small fraction of the cardinality of the dataset.

This may be advantageous because by comparison with the one branch per value case, during the training process more test cases are available at each of the lower nodes to use in decision making, possibly increasing accuracy. D. Wang and M. ): AI 2011, LNAI 7106, pp. 31–40, 2011. c Springer-Verlag Berlin Heidelberg 2011 32 A. Berry and M. Cameron-Jones Several researchers have looked at the issue of ordinal class values in general [4,5]. Ordinal monotonicity, where class and attribute value orders have an enforced relationship, has also been a very active field of research [1,7].

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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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