Uncertainty and intelligent information systems / editors, Bernadette Bouchon-Meunier ... [et al.].

Format:

Book

Conference/Event

Contributor:

Bouchon-Meunier, B. (Bernadette), 1948-

Conference Name:

International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (2006 : Paris, France)

International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems

Language:

English

Subjects (All):

Expert systems (Computer science)--Congresses.

Expert systems (Computer science).

Uncertainty (Information theory)--Congresses.

Uncertainty (Information theory).

Physical Description:

1 online resource (536 p.)

Place of Publication:

Singapore ; Hackensack, NJ : World Scientific, c2008.

Language Note:

English

Summary:

Intelligent systems are necessary to handle modern computer-based technologies managing information and knowledge. This book discusses the theories required to help provide solutions to difficult problems in the construction of intelligent systems. Particular attention is paid to situations in which the available information and data may be imprecise, uncertain, incomplete or of a linguistic nature. The main aspects of clustering, classification, summarization, decision making and systems modeling are also addressed. Topics covered in the book include fundamental issues in uncertainty, the rap

Contents:

Foreword; Contents; Uncertainty Modeling; Chapter 1. The Game-Theoretic Framework for Probability; 1. Introduction; 2. The Origins of Cournot's Principle; 3. Ville's Theorem; 4. The Game-Theoretic Framework; 5. Extending the Classical Limit Theorems; 6. The Idea of a Quasi-Universal Test; 7. Defensive Forecasting; References; Chapter 2. Aggregated Likelihoods: a Comparative Approach; 1. Introduction; 2. Coherent Conditional Probabilities; 3. Comparative Relations; References; Chapter 3. The Moment Problem for Finitely Additive Probabilities; 1. Introduction

2. A Short Introduction to Lower Previsions3. Formulation and Initial Solution of the Problem; 4. The Natural Extension Em and m-Integrable Gambles; 5. The Natural Extension of Lower and Upper Distribution Functions; 5.1. A precise distribution function; 5.2. Lower and upper distribution functions; 6. The Information Given by the Lower and the Upper Distribution Functions; 7. Conclusions; Acknowledgments; References; Chapter 4. Towards a General Theory of Conditional Decomposable Information Measures; 1. Introduction; 2. Kamp ́e de F ́eriet Information Measures; 3. Conditional Events

4. From Conditional Events to Conditional Information Measures5. Coherent Conditional Information Measures and Their Characterization; References; Chapter 5. Discourse Interpretation as Model Selection - A Probabilistic Approach; 1. Introduction; 2. What is an Interpretation?; 3. Proposing Interpretations; 4. Probabilistic Formalism; 4.1. Prior probability of an interpretation; 4.2. Data Fit Between the Discourse and an Interpretation; 4.3. Accounting for the Components of an Interpretation; 5. Conclusion; Acknowledgments; References

Chapter 6. Elicitation of Expert Opinions for Constructing Belief Functions1. Introduction; 2. Background; 2.1. The transferable belief model; 2.1.1. Credal level; 2.1.2. Pignistic level; 2.2. Uncertainty measures; 2.2.1. Nonspecificity measures; 2.2.2. Conflict measures; 2.2.3. Composite measures; 2.3. Least commitment principle; 3. Previous Works; 3.1. Wong and Lingras' method; 3.2. Bryson et al.' method; 4. Constructing Belief Functions from Qualitative Preferences; 4.1. Main ideas; 4.2. Mono-objective optimization model; 4.3. Multiobjective optimization models; 5. Conclusion

AcknowledgmentsReferences; Chapter 7. Managing Decomposed Belief Functions; 1. Introduction; 2. Decomposition; 3. Combining Simple Support Functions and Inverse Simple Support Functions; 3.1. Two SSFs; 3.2. One SSF and one ISSF; 3.3. Two ISSFs; 4. Clustering SSFs and ISSFs; 5. Conclusions; Acknowledgment; References; Clustering, Classification and Summarization; Chapter 8. Generalized Naive Bayesian Modeling; 1. The Naive Bayesian Classifier; 2. t-OWA Operators; 3. An Extended Bayesian Classifier; 4. Algorithm for Learning Weights; 5. An Illustrative Example; 6. Retaining the Meanness

7. Conclusion

Notes:

" ... draws on papers presented at the 2006 Conference on Information Processing and Management of Uncertainty (IPMU) which was held in Paris in 2006."--P. v.

Includes bibliographical references.

ISBN:

9789812792358

981279235X