Get Artificial Intelligence and Soft Computing: 15th PDF

By Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada

ISBN-10: 3319393839

ISBN-13: 9783319393834

ISBN-10: 3319393847

ISBN-13: 9783319393841

The two-volume set LNAI 9692 and LNAI 9693 constitutes the refereed lawsuits of the fifteenth foreign convention on man made Intelligence and gentle Computing, ICAISC 2016, held in Zakopane, Poland in June 2016.
The 134 revised complete papers provided have been conscientiously reviewed and chosen from 343 submissions. The papers incorporated within the first quantity are prepared within the following topical sections: neural networks and their functions; fuzzy structures and their purposes; evolutionary algorithms and their functions; agent structures, robotics and regulate; and trend type. the second one quantity is split within the following components: bioinformatics, biometrics and scientific functions; facts mining; synthetic intelligence in modeling and simulation; visible details coding meets computing device studying; and numerous difficulties of man-made intelligence.

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Read Online or Download Artificial Intelligence and Soft Computing: 15th International Conference, ICAISC 2016, Zakopane, Poland, June 12-16, 2016, Proceedings, Part II PDF

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Extra info for Artificial Intelligence and Soft Computing: 15th International Conference, ICAISC 2016, Zakopane, Poland, June 12-16, 2016, Proceedings, Part II

Example text

Finally, we used the Random tree generator, that produces concepts that theoretically should favor decision tree learners. It constructs decision tree randomly according to several parameter settings, as number of classes, number of categorical and numerical attributes. The tree is used to determine the class label for new examples generated by assigning an uniformly distributed random value to the attributes. The attribute values as well as the class label determined via the tree become part of the data stream.

FCP IDs Itemsets (FCIs) 1 2 3 4 5 6 7 {P21 , P22 , P13 , {P21 , P22 , P23 , {P21 , P22 , P33 , {P11 , P12 , P13 , {P21 , P22 , P14 , {P13 } {P21 , P22 } P14 , P15 } P14 , P15 } P34 , P35 } P24 , P25 } P15 } Instance IDs {4, 5} {6, 7} {8, 9} {1, 2, 3} {4, 5, 6, 7} {1, 2, 3, 4, 5} {4, 5, 6, 7, 8, 9} number of base clusterings. , covering all dataset instances: Definition 4. , Pm } and the definition BDT = IDT ∪ PDT +1 where IDT is the instance sets of the FCPs built from DT base clusterings, and PDT +1 is the instance sets (clusters) of the previous consensus.

Data Sci. 2(2), 165–193 (2015) 23. : The complexity of mining maximal frequent itemsets and maximal frequent patterns. In: ACM SIGKDD, pp. 344–353 (2004) 24. : Consensus clustering + meta clustering = multiple consensus clustering. In: Proceedings of the FLAIRS Conference (2011) Complexity of Rule Sets Induced from Data Sets with Many Lost and Attribute-Concept Values Patrick G. Clark1 , Cheng Gao1 , and Jerzy W. edu 2 Department of Expert Systems and Artificial Intelligence, University of Information Technology and Management, 35-225 Rzeszow, Poland Abstract.

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Artificial Intelligence and Soft Computing: 15th International Conference, ICAISC 2016, Zakopane, Poland, June 12-16, 2016, Proceedings, Part II by Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada


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