Advanced Machine Learning Technologies and Applications: by Hiroshi Sakai, Mao Wu, Michinori Nakata, Dominik Ślęzak

By Hiroshi Sakai, Mao Wu, Michinori Nakata, Dominik Ślęzak (auth.), Aboul Ella Hassanien, Abdel-Badeeh M. Salem, Rabie Ramadan, Tai-hoon Kim (eds.)

This publication constitutes the refereed lawsuits of the 1st foreign convention on complex desktop studying applied sciences and purposes, AMLTA 2012, held in Cairo, Egypt, in December 2012. The fifty eight complete papers offered have been rigorously reviewed and chosen from ninety nine intial submissions. The papers are equipped in topical sections on tough units and purposes, laptop studying in trend popularity and photograph processing, desktop studying in multimedia computing, bioinformatics and cheminformatics, info type and clustering, cloud computing and recommender systems.

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Extra resources for Advanced Machine Learning Technologies and Applications: First International Conference, AMLTA 2012, Cairo, Egypt, December 8-10, 2012. Proceedings

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Possible Equivalence Relations and Their Application to Hypothesis Generation in Non-deterministic Information Systems. , Polkowski, L. ) Transactions on Rough Sets II. LNCS, vol. 3135, pp. 82–106. Springer, Heidelberg (2004) 13. : Rules and Apriori Algorithm in Non-deterministic Information Systems. , Rybiński, H. ) Transactions on Rough Sets IX. LNCS, vol. 5390, pp. 328–350. Springer, Heidelberg (2008) 14. : Rough sets based rule generation from data with categorical and numerical values. Journal of Advanced Computational Intelligence and Intelligent Informatics 12(5), 426–434 (2008) 15.

220–226 (2000) 6. : From Data Mining to Knowledge Discovery in Databases. AI Magazine 17(3), 37–54 (1996) 7. : Rough Sets Theory for Multicriteria Decision Analysis. European Journal of Operational Research 129(1), 1–47 (2001) 8. : An Introduction to Variable and Feature Selection. Journal of Machine Learning Research 3, 1157–1182 (2003) 22 D. Ślęzak and P. Betliński 9. : The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998) 10.

Thus, the transition t1 is enabled by M 0 and v = 1. 53, 0) (Figure 3(b)). It is easy to check that by the initial marking M 0 and v = 1 the transition t2 is not enabled. 40 4 Z. Suraj Illustrating Example In this section we present an application of PFPN in the domain of train traffic control [15]. The considered example is based on a simplified version of the real-life problem. We assume the following situation: a train B waits at a certain station for a train A to arrive in order to allow some passengers to change train A to train B.

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