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Transactions On Rough Sets Ix 2009 Edition at Meripustak

Transactions On Rough Sets Ix 2009 Edition by James F. Peters Andrzej Skowron Henryk Rybinski , Springer

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  • General Information  
    Author(s) James F. Peters Andrzej Skowron Henryk Rybinski
    PublisherSpringer
    ISBN9783540898757
    Pages751
    BindingPaperback
    LanguageEnglish
    Publish YearJanuary 2009

    Description

    Springer Transactions On Rough Sets Ix 2009 Edition by James F. Peters Andrzej Skowron Henryk Rybinski

    Volume IX of the Transactions on Rough Sets (TRS) provides evidence of the continuing growth of a number of research streams that were either directly or indirectly begun by the seminal work on rough sets by Zdzis law Pawlak (1926- 1 2006) .OneoftheseresearchstreamsinspiredbyProf.Pawlakisroughset-based intelligent systems a topic that was an important part of his early 1970s work on knowledge description systems prior to his discovery of rough sets during the early 1980s. Evidence of intelligent systems as a recurring motif over the past twodecadescanbefoundintherough-setliteraturethatnowincludesover4000 2 publications by more than 1600 authors in the rough set database . This volume of the TRS includes articles that are extensions of papers in- 3 cludedinthe?rstconferenceonRoughSetsandIntelligentSystemsParadigms . In addition to research on intelligent systems this volume also presents papers that re?ect the profound in?uence of a number of other research initiatives by Zdzis law Pawlak. In particular this volume introduces a number of new advances in the fo- dations and applications of arti?cial intelligence engineering image processing logic mathematics medicine music and science.These advances have sign- icant implications in a number of research areas such as attribute reduction approximation schemes category-based inductive reasoning classi?ers classi- ing mappings context algebras data mining decision attributes decision rules decision support diagnostic feature analysis EEG classi?cation feature ana- sis granular computing hierarchical classi?ers indiscernibility relations inf- mationgranulationinformationsystemsmusicalrhythm retrievalprobabilistic dependencies reducts rough-fuzzy C-means rough inclusion functions rou- ness singing voice recognition and vagueness. A total of 47 researchers are represented in this volume. Table of 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.



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