Titlebook: Uncertainty Management with Fuzzy and Rough Sets; Recent Advances and Rafael Bello,Rafael Falcon,José Luis Verdegay Book 2019 Springer Nat

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In-Database Rule Learning Under Uncertainty: A Variable Precision Rough Set Approachase rule learner for nonstationary environments in this chapter. The assessment under different scenarios with other state-of-the-art rule inducers demonstrate the algorithm is comparable with existing methods, but superior when applied to critical applications that anticipate further confidence fro

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tivation to undertake this new edition of the book. This edition is intended to enable readers to understand the current state-of-the-art in this field. More than 20 percent of the material in this edition is new, including a chapter on systems with iterative channel estimation, and the remainder of the material has been thoroughly revised..

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Elio H. Cables,María Teresa Lamata,José Luis Verdegayh turbulence is described in detail using Kolmogorov statistics,..the impact of turbulence on the imaging process is discussed both for single telescopes and for interferometers,..instrumental techniques like beam combination and array layout are described, and the requirements for delay lines are d

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Frank Beer,Ulrich Bühlercosystem ecology to advanced undergraduate students, beginning graduate students, and practicing scientists from a wide array of disciplines. We also provide access to some of the rapidly expanding literature in the many disciplines that contribute to ecosystem understanding.978-0-387-21663-8

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Marilyn Bello,Gonzalo Nápoles,Ivett Fuentes,Isel Grau,Rafael Falcon,Rafael Bello,Koen Vanhoofwering precisely when a TM implementation is correct, what kind of properties it can ensure, what are the power and limitations of a TM, and what inherent trade-offs are involved in designing a TM algorithm. While the focus of this book is on the fundamental principles, its goal is to capture the co