Cantitate/Preț
Produs

Rough Set Theory: A True Landmark in Data Analysis: Studies in Computational Intelligence, cartea 174

Editat de Ajith Abraham, Rafael Falcón, Rafael Bello
en Limba Engleză Hardback – 26 feb 2009
Along the years, rough set theory has earned a well-deserved reputation as a sound methodology for dealing with imperfect knowledge in a simple though mathematically sound way. This edited volume aims at continue stressing the benefits of applying rough sets in many real-life situations while still keeping an eye on topological aspects of the theory as well as strengthening its linkage with other soft computing paradigms. The volume comprises 11 chapters and is organized into three parts. Part 1 deals with theoretical contributions while Parts 2 and 3 focus on several real world data mining applications. Chapters authored by pioneers were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed. Academics, scientists as well as engineers working in the rough set, computational intelligence, soft computing and data mining research area will find the comprehensive coverage of this book invaluable.
Citește tot Restrânge

Toate formatele și edițiile

Toate formatele și edițiile Preț Express
Paperback (1) 96909 lei  6-8 săpt.
  Springer Berlin, Heidelberg – 28 oct 2010 96909 lei  6-8 săpt.
Hardback (1) 97539 lei  6-8 săpt.
  Springer Berlin, Heidelberg – 26 feb 2009 97539 lei  6-8 săpt.

Din seria Studies in Computational Intelligence

Preț: 97539 lei

Preț vechi: 121923 lei
-20% Nou

Puncte Express: 1463

Preț estimativ în valută:
18667 19390$ 15506£

Carte tipărită la comandă

Livrare economică 01-15 februarie 25

Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9783540899204
ISBN-10: 3540899200
Pagini: 340
Ilustrații: XVI, 324 p.
Dimensiuni: 155 x 235 x 24 mm
Greutate: 0.65 kg
Ediția:2009
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Studies in Computational Intelligence

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

Theoretical Contributions to Rough Set Theory.- Rough Sets on Fuzzy Approximation Spaces and Intuitionistic Fuzzy Approximation Spaces.- Categorical Innovations for Rough Sets.- Granular Structures and Approximations in Rough Sets and Knowledge Spaces.- On Approximation of Classifications, Rough Equalities and Rough Equivalences.- Rough Set Data Mining Activities.- Rough Clustering with Partial Supervision.- A Generic Scheme for Generating Prediction Rules Using Rough Sets.- Rough Web Caching.- Software Defect Classification: A Comparative Study of Rough-Neuro-fuzzy Hybrid Approaches with Linear and Non-linear SVMs.- Rough Hybrid Models to Classification and Attribute Reduction.- Rough Sets and Evolutionary Computation to Solve the Feature Selection Problem.- Nature Inspired Population-Based Heuristics for Rough Set Reduction.- Developing a Knowledge-Based System Using Rough Set Theory and Genetic Algorithms for Substation Fault Diagnosis.

Textul de pe ultima copertă

Along the years, rough set theory has earned a well-deserved reputation as a sound methodology for dealing with imperfect knowledge in a simple though mathematically sound way. This edited volume aims at continue stressing the benefits of applying rough sets in many real-life situations while still keeping an eye on topological aspects of the theory as well as strengthening its linkage with other soft computing paradigms. The volume comprises 11 chapters and is organized into three parts. Part 1 deals with theoretical contributions while Parts 2 and 3 focus on several real world data mining applications. Chapters authored by pioneers were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed. Academics, scientists as well as engineers working in the rough set, computational intelligence, soft computing and data mining research area will find the comprehensive coverage of this book invaluable.

Caracteristici

Reports recent research results in rough set research Includes supplementary material: sn.pub/extras