Download Intelligent Computing Theories and Applications: 8th by Xiaoming Liu, Bo Li, Jun Liu, Xin Xu, Zhilin Feng (auth.), PDF

By Xiaoming Liu, Bo Li, Jun Liu, Xin Xu, Zhilin Feng (auth.), De-Shuang Huang, Jianhua Ma, Kang-Hyun Jo, M. Michael Gromiha (eds.)

This publication constitutes the refereed lawsuits of the eighth foreign convention on clever Computing, ICIC 2012, held in Huangshan, China, in July 2012.
The eighty five revised complete papers provided have been conscientiously reviewed and chosen from 753 submissions. The papers are prepared in topical sections on neural networks, evolutionar studying and genetic algorithms, granular computing and tough units, biology encouraged computing and optimization, nature encouraged computing and optimization, cognitive technological know-how and computational neuroscience, wisdom discovery and information mining, quantum computing, computer studying conception and techniques, healthcare informatics concept and techniques, biomedical informatics concept and strategies, advanced platforms conception and strategies, clever computing in sign processing, clever computing in picture processing, clever computing in robotics, clever computing in laptop imaginative and prescient, clever agent and internet functions, unique consultation on advances in details protection 2012.

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Extra info for Intelligent Computing Theories and Applications: 8th International Conference, ICIC 2012, Huangshan, China, July 25-29, 2012. Proceedings

Sample text

That is only to collect a subset of the network [3]. It has selectively collected on the network associated with the predetermined topic pages. Topic, that is * Corresponding author. -S. Huang et al. ): ICIC 2012, LNAI 7390, pp. 35–42, 2012. -H. Zhang and F. Zhang the user defined type to be collected can be specified by the user to determine the series of web pages, you can also describe the types of features by a series of words to determine the characteristics. The main technologies involved in its information mining, text classification, text extraction, network collection, etc.

Rough fuzzy sets and fuzzy rough sets. International Journal of General Systems 17, 191–209 (1990) 3. : Uncertainty measures of rough set prediction. Artificial Intelligence 106, 109–137 (1998) 4. : Rough approximation quality revisited. Artificial Intelligence 132, 219–234 (2001) 5. : Fuzzy-rough sets assisted attribute selection. IEEE Transactions on Fuzzy Systems 15(1), 73–89 (2007) 6. : A new method for measuring uncertainty and fuzziness in rough set theory. International Journal of General Systems 31(4), 331–342 (2002) 7.

Let S = (U, AT ∪ {d}, V, f, g) be an interval ordered information system and X, Y ⊆ U , A ⊆ AT , then its lower and upper approximations satisfy the following properties. ≥ ≥ (X) ⊆ X ⊆ RA (X). (1) RA ≥ ≥ ≥ ≥ ≥ ≥ (2) RA (X ∩ Y ) = RA (X) ∩ RA (Y ); RA (X ∪ Y ) = RA (X) ∪ RA (Y ). ≥ ≥ ≥ ≥ ≥ ≥ (X ∪ Y ) ⊇ RA (X) ∪ RA (Y ); RA (X ∩ Y ) ⊆ RA (X) ∩ RA (Y ). (3) RA ≥ ≥ ≥ ≥ (4) RA (X) =∼ RA (∼ X); RA (X) =∼ RA (∼ X). ≥ ≥ ≥ ≥ (5) RA (∅) = RA (∅) = ∅; RA (U ) = RA (U ) = U . ≥ ≥ ≥ ≥ ≥ ≥ (6) RA (RA (X)) = RA (X); RA (RA (X)) = RA (X).

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