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Transactions on Rough Sets IX

Author: David HutchisonHenryk RybińskiAndrzej SkowronGerhard WeikumMoshe Y VardiAll authors
Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.
Series: Lecture notes in computer science, 5390.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Database:WorldCat
Summary:
Annotation The LNCS journal Transactions on Rough Sets is devoted to the entire spectrum of rough sets related issues, from logical and mathematical foundations, through all aspects of rough set theory and its applications, such as data mining, knowledge discovery, and intelligent information processing, to relations between rough sets and other approaches to uncertainty, vagueness, and incompleteness, such as fuzzy  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Transactions on rough sets IX.
Berlin ; New York : Springer, ©2008
(OCoLC)310979560
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: David Hutchison; Henryk Rybiński; Andrzej Skowron; Gerhard Weikum; Moshe Y Vardi; Doug Tygar; Demetri Terzopoulos; Madhu Sudan; Takeo Kanade; C Pandu Rangan; Oscar Nierstrasz; Moni Naor; John C Mitchell; Friedemann Mattern; Jon M Kleinberg; Josef Kittler; James F Peters; Bernhard Steffen
ISBN: 9783540898764 354089876X 3540898751 9783540898757
OCLC Number: 311303760
Description: 1 online resource (: v.: digital.
Contents: Vagueness and Roughness --
Modified Indiscernibility Relation in the Theory of Rough Sets with Real-Valued Attributes: Application to Recognition of Fraunhofer Diffraction Patterns --
On Certain Rough Inclusion Functions --
Automatic Rhythm Retrieval from Musical Files --
FUN: Fast Discovery of Minimal Sets of Attributes Functionally Determining a Decision Attribute --
Information Granulation: A Medical Case Study --
Maximum Class Separability for Rough-Fuzzy C-Means Based Brain MR Image Segmentation --
Approximation Schemes in Logic and Artificial Intelligence --
Decision Rule Based Data Models Using NetTRS System Overview --
A Rough Set Based Approach for ECG Classification --
Universal Problem of Attribute Reduction --
Extracting Relevant Information about Reduct Sets from Data Tables --
Context Algebras, Context Frames, and Their Discrete Duality --
A Study in Granular Computing: On Classifiers Induced from Granular Reflections of Data --
On Classifying Mappings Induced by Granular Structures --
The Neurophysiological Bases of Cognitive Computation Using Rough Set Theory --
Diagnostic Feature Analysis of a Dobutamine Stress Echocardiography Dataset Using Rough Sets --
Rules and Apriori Algorithm in Non-deterministic Information Systems --
On Extension of Dependency and Consistency Degrees of Two Knowledges Represented by Covering --
A New Approach to Distributed Algorithms for Reduct Calculation --
From Information System to Decision Support System --
Debellor: A Data Mining Platform with Stream Architecture --
Category-Based Inductive Reasoning: Rough Set Theoretic Approach --
Probabilistic Dependencies in Linear Hierarchies of Decision Tables --
Automatic Singing Voice Recognition Employing Neural Networks and Rough Sets --
Hierarchical Classifiers for Complex Spatio-temporal Concepts.
Series Title: Lecture notes in computer science, 5390.
Responsibility: edited by David Hutchison, Takeo Kanade, Josef Kittler, Jon M. Kleinberg, Friedemann Mattern, John C. Mitchell, Moni Naor, Oscar Nierstrasz, C. Pandu Rangan, Bernhard Steffen, Madhu Sudan, Demetri Terzopoulos, Doug Tygar, Moshe Y. Vardi, Gerhard Weikum, James F. Peters, Andrzej Skowron, Henryk Rybiński.

Abstract:

This book is the ninth volume of the Transactions on Rough Sets series. The 26 papers in it introduce new advances in the foundations and applications of artificial intelligence, engineering, image  Read more...

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From the reviews: "Pawlak originally developed rough sets to be used in the same situations as Zadeh's better-known fuzzy sets ... . Although they are less popular than fuzzy sets, they do have a Read more...

 
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