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Z. Pawlak: Foreword; L. Polkowski, A. Skowron: Introducing the Book.- Applications: S. Greco, B. Matarazzo, R. Slowinski: Rough Approximation of a Preference Relation in a Pairwise Comparison Table; K. Krawiec, R. Slowinski, D. Vanderpooten: Learning Decision Rules form Similiarity Based Rough Approximations; S. Hoa Nguyen, A. Skowron, P. Synak: Discovery of Data Patterns with Applications to Decomposition and Classification Problems; Z.W. Ras: Answering Non-Standard Queries in Distributed Knowledge-Based Systems; J. Stepaniuk: Approximation Spaces, Reducts and Representatives; N. Zhong, J.Z. Dong, S. Ohsuga: Data Mining: A Probabilistic Rough Set Approach.- Case Studies: A. Czyzewski: Soft Processing of Audio Signals; K. Furuta, M. Hirokane, Y. Mikumo: Extraction Method Based on Rough Set Theory of Rule-Type Knowledge from Diagnostic Cases of Slope-Failure Danger Levels; B. Kostek: Soft Computing-Based Recognition of Musical Sounds; A. Mrozel, K. Skabek: Rough Sets in Economic Applixations; K. Slowinski, J. Stefanowski: Multistage Rough Set Analysis of Therapeutic Experience with Acute Pancreatitis; H. Tanaka, Y. Maeda: Reduction Methods for Medical Data; S. Tsumoto: Formalization and Induction of Medical Expert System Rules Based on Rough Set Theory; D. Van den Poel: Rough Sets for Database Marketing; H. Zang, R. Swiniarski: A New Halftoning Method Based on Error Diffusion with Rough Set Filtering.- Hybrid Approaches: C. Browne, I. Düntsch, G. gediga: IRIS Revisited: A Comparison of Discriminant and Enhanced Rough Set Data Analysis; R. Lingras: Applications of Rough Patterns; J.F. Peters III: Time and Clock Information Systems: Concepts and Roughly Fuzzy Petri Net Models; Z. Suraj: The Synthesis Problem of Concurrent Systems Specified by Dynamic Information Systems; M.S. Szczuka: Rough Sets and Artificial Neural Networks; J. Wróblewski: Genetic Algorithms in Decomposition and Classification Problems.- Appendix 1: Rough Set Bibliography.- Appendix 2: Software Systems.
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Add Rough Sets in Knowledge Discovery 2: Applications, Case Studies and Software Systems, The ideas and techniques worked out in Rough Set Theory allow for knowledge reduction and to finding near - to - functional dependencies in data. This fact determines the importance of these techniques for the rapidly growing field of knowledge discovery., Rough Sets in Knowledge Discovery 2: Applications, Case Studies and Software Systems to the inventory that you are selling on WonderClubX
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Add Rough Sets in Knowledge Discovery 2: Applications, Case Studies and Software Systems, The ideas and techniques worked out in Rough Set Theory allow for knowledge reduction and to finding near - to - functional dependencies in data. This fact determines the importance of these techniques for the rapidly growing field of knowledge discovery., Rough Sets in Knowledge Discovery 2: Applications, Case Studies and Software Systems to your collection on WonderClub |