New PDF release: Artificial Intelligence and Soft Computing: 15th
By Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada
The two-volume set LNAI 9692 and LNAI 9693 constitutes the refereed lawsuits of the fifteenth foreign convention on synthetic Intelligence and tender Computing, ICAISC 2016, held in Zakopane, Poland in June 2016.
The 134 revised complete papers awarded have been rigorously reviewed and chosen from 343 submissions. The papers incorporated within the first quantity are prepared within the following topical sections: neural networks and their purposes; fuzzy structures and their purposes; evolutionary algorithms and their purposes; agent structures, robotics and keep watch over; and trend class. the second one quantity is split within the following components: bioinformatics, biometrics and clinical purposes; information mining; synthetic intelligence in modeling and simulation; visible details coding meets laptop studying; and numerous difficulties of synthetic intelligence.
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
1. Number of rules for the breast cancer data set Fig. 2. Number of rules for the echocardiogram data set Fig. 3. Number of rules for the hepatitis data set Fig. 4. G. Clark et al. Fig. 5. Number of rules for the lymphography data set Fig. 6. Number of rules for the wine recognition data set Fig. 7. Total number of conditions for the breast cancer data set Fig. 8. Total number of conditions for the echocardiogram data set Fig. 9. Total number of conditions for the hepatitis data set Fig. 10. Total number of conditions for the image data set 3 Probabilistic Approximations In this section deﬁnitions of singleton, subset and concept approximations are extended to the corresponding probabilistic approximations.
Knowledge Discovery from Data Streams. Chapman and Hall/CRC, Boca Raton (2010) 9. : A survey on learning from data streams: current and future trends. Prog. Artif. Intell. 1, 45–55 (2012) 10. : Mining time-changing data streams. In: KDD-01, pp. 97–106. ACM Press (2001) 11. : A sequential algorithm for training text classifiers. In: Proceedings of the ACM SIGIR Conference, pp. 3–12 (1994) 12. : Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison (2009) 13.
Each time the two considered vertices are clustered together by any base clustering, their similarity is increment by 1), then a graph-based clustering method (METIS) provides the consensus clustering; (ii) HyperGraphPartitioning Algorithm (HGPA) all hyperedges as well as all vertices are equally weighted, then a hypergraph partitioning algorithm (HMETIS) deﬁnes the consensus by cutting a minimal number of edges; (iii) Meta-CLustering Algorithm (MCLA) follow the same ideas, but hyperedges weights are proportional to the similarity between vertices (instances) which is calculated using binary Jaccard measure.
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