Modern Information Processing

1st Edition

From Theory to Applications

Editors: Bernadette Bouchon-Meunier Giulianella Coletti Ronald R. Yager
Hardcover ISBN: 9780444520753
eBook ISBN: 9780080461694
Imprint: Elsevier Science
Published Date: 8th February 2006
Page Count: 478
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Description

The volume "Modern Information Processing: From Theory to Applications," edited by Bernadette Bouchon-Meunier, Giulianella Coletti and Ronald Yager, is a collection of carefully selected papers drawn from the program of IPMU'04, which was held in Perugia, Italy.
The book represents the cultural policy of IPMU conference which is not focused on narrow range of methodologies, but on the contrary welcomes all the theories for the management of uncertainty and aggregation of information in intelligent systems, providing a medium for the exchange of ideas between theoreticians and practitioners in these and related areas.
The book is composed by 7 sections:
UNCERTAINTY PREFERENCES CLASSIFICATION AND DATA MINING AGGREGATION AND MULTI-CRITERIA DECISION MAKING KNOWLEDGE REPRESENTATION

The book contributes to enhancement of our ability to deal effectively with uncertainty in all of its manifestations. The book can help to build brigs among theories and methods methods for the management of uncertainty. The book addresses issues which have a position of centrality in our information-centric world. The book presents interesting results devoted to representing knowledge: the goal is to capture the subtlety of human knowledge (richness) and to allow computer manipulation (formalization). The book contributes to the goal: an efficient use of the information for a good decision strategy. APPLIED DOMAINS

Key Features

· The book contributes to enhancement of our ability to deal effectively with uncertainty in all of its manifestations. · The book can help to build brigs among theories and methods methods for the management of uncertainty. · The book addresses issues which have a position of centrality in our information-centric world. · The book presents interesting results devoted to representing knowledge: the goal is to capture the subtlety of human knowledge (richness) and to allow computer manipulation (formalization). · The book contributes to the goal: an efficient use of the information for a good decision strategy.

Readership

Researchers oriented to theory and application of methods for handling partial knowledge in intelligent systems. Also for Ph.D. students in mathematics and computer science. Nevertheless, the book is intended for a wider audience ranging from graduate students with proper background in mathematics and/or informatics.

Table of Contents

Foreword. L.A. Zadeh


Uncertainty


Entropies, Characterizations, Applications and Some History, J. Aczél


Belief function theory on the continuous space with an application to model based classification, B. Ristic, P.Smets


Independence in conditional possibility theory, G. Coletti, B. Vantaggi


Joint treatment of imprecision and randomness in uncertainty propagation, C. Baudrit, D. Dubois, D. Guyonnet, H. Fargier


Consistency of probabilistic transformations of belief functions, M. Daniel


Randomization and uncertain inference, H. E. Kyburg, Jr., C. M. Teng


An empirical complexity study for a 2CPA solver, M. Baioletti, A. Capotorti, S. Tulipani


Preferences


Consistency in preference modelling, J-L. García-Lapresta, J. Montero


Transitive decomposition of min-transitive fuzzy preference relations, S. Díaz, B. De Baets, S. Montes


Decision making with fuzzy ternary relations, S. Ovchinnikov


New Consistency properties for preference relations, F. Chiclana, E. Herrera-Viedma, F. Herrera


Management of uncertainty orderings through ASP, A. Capotorti, A. Formisano


Classification and Data Mining


Automating the quality assurance of an on-line knowledge-based classifier by fusing multiple off-line classifiers, P. Bonissone


Qualitative classification with possibilistic decision trees, N. Ben Amor, S. Benferhat, Z.Elouedi


Discovery of abstract knowledge from non-atomic attribute values in fuzzy relations, R. A. Angryk, F. E. Petry


Kernel-based outlier preserving clustering with representativity coefficients, M.J. Lesot


Fuzzy C-medoids clustering models for time-varying data, R. Coppi, P. D’Urso, P. Giordani


Improving t

Details

No. of pages:
478
Language:
English
Copyright:
© Elsevier Science 2006
Published:
Imprint:
Elsevier Science
eBook ISBN:
9780080461694
Hardcover ISBN:
9780444520753

About the Editor

Bernadette Bouchon-Meunier

Affiliations and Expertise

CNRS-UPMCS, LIP6, Paris, France

Giulianella Coletti

Affiliations and Expertise

University of Perugia, Italy

Ronald R. Yager

Affiliations and Expertise

Iona College, Machine Intelligence Institute, New York, U.S.A.

Reviews

The volume "Modern Information Processing: From Theory to Applications," edited by Bernadette Bouchon-Meunier, Giulianella Coletti and Ronald Yager, is a collection of carefully selected papers drawn from the program of IPMU'04, which was held in Perugia, Italy.
The book represents the cultural policy of IPMU conference which is not focused on narrow range of methodologies, but on the contrary welcomes all the theories for the management of uncertainty and aggregation of information in intelligent systems, providing a medium for the exchange of ideas between theoreticians and practitioners in these and related areas.
The book is composed by 7 sections:
UNCERTAINTY PREFERENCES CLASSIFICATION AND DATA MINING AGGREGATION AND MULTI-CRITERIA DECISION MAKING KNOWLEDGE REPRESENTATION The book contributes to enhancement of our ability to deal effectively with uncertainty in all of its manifestations. The book can help to build brigs among theories and methods methods for the management of uncertainty. The book addresses issues which have a position of centrality in our information-centric world. The book presents interesting results devoted to representing knowledge: the goal is to capture the subtlety of human knowledge (richness) and to allow computer manipulation (formalization). The book contributes to the goal: an efficient use of the information for a good decision strategy. APPLIED DOMAINS