Screen LibraryUncertainty and intelligent information systems /
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...
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| Corporate Authors: | ; |
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| Group Author: | |
| Published: |
World Scientific Pub. Co.,
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| Publisher Address: | Singapore ; Hackensack, N.J. : |
| Publication Dates: | 2008. |
| Literature type: | eBook |
| Language: | English |
| Subjects: | |
| Online Access: |
http://www.worldscientific.com/worldscibooks/10.1142/6747#t=toc |
| 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 rapidly emerging discipline of information aggregation, neural networks, Bayesian networks and other network methods, as well as logic-based systems. |
| Item Description: | " ... 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. |
| Carrier Form: | 1 online resource (xxi,514pages) : illustrations |
| Bibliography: | Includes bibliographical references. |
| ISBN: | 9789812792358 (electronic bk.) |
| CLC: | TP11 |
| Contents: |
Uncertainty modeling. ch. 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. ch. 2. Aggregated likelihoods: a comparative approach. 1. Introduction. 2. Coherent conditional probabilities. 3. Comparative relations. ch. 3. The moment problem for finitely additive probabilities. 1. Introduction. 2. A short introduction to lower previsions. 3. Formulation and initial solution of the problem. 4. The natural extension [symbol] and m-integrable gambles. 5. The natural extension of lower and upper distribution functions. 6. The information given by the lower and the upper distribution functions. 7. Conclusions. ch. 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 measures. 5. Coherent conditional information measures and their characterization. ch. 5. Discourse interpretation as model selection - a probabilistic approach. 1. Introduction. 2. What is an interpretation? 3. Proposing interpretations. 4. Probabilistic formalism. 5. Conclusion. ch. 6. Elicitation of expert opinions for constructing belief functions. 1. Introduction. 2. Background. 3. Previous works. 4. Constructing belief functions from qualitative preferences. 5. Conclusion. ch. 7. Managing decomposed belief functions. 1. Introduction. 2. Decomposition. 3. Combining simple support functions and inverse simple support functions. 4. Clustering SSFs and ISSFs. 5. Conclusions -- Clustering, classification and summarizationch. ch. 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. ch. 9. Gustafson-Kessel-like clustering. 1. Introduction. 2. Fuzzy clustering. 3. Typicality degrees. 4. Typicality degrees for clustering. 5. Numerical experiments. 6. Conclusion. ch. 10. A hierarchical immune-inspired approach for text clustering. 1. Introduction. 2. Semantic SOM. 3. Adaptive radius immune algorithm (ARIA). 4. Similarity metric. 5. Computational experiments. 6. Discussion. ch. 11. An incremental hierarchical fuzzy clustering for category-based news filtering. 1. Introduction. 2. History and applications of dissociative recombination. 3. The rationale of the proposal. 4. The hierarchical fuzzy clustering algorithm. 5. Evaluation results. 6. Conclusions. ch. 12. Soft mapping between hierarchical classifications. 1. Introduction. 2. Instance matching. 3. Hierarchy matching. 4. Application to film databases. 5. Summary. ch. 13. On linguistic summarization of time series using fuzzy logic with linguistic quantifiers. 1. Introduction. 2. Temporal data and trend analysis. 3. Dynamic characteristics of trends. 4. Linguistic data summaries. 5. Protoforms of linguistic trend summaries. 6. The use of Zadeh's calculus of linguistically quantified propositions. 7. Numerical experiments. 8. Concluding remarks. ch. 14. A possible worlds interpretation of label semantics. 1. Introduction. 2. The label semantics framework. 3. The possible worlds model. 4. Conclusions -- Decision making and information processing. ch. 15. Definition of an importance index for bi-capacities in multi-criteria decision analysis. 1. Introduction. 2. Preliminaries. 3. Definition of a value for bi-cooperative games. 4. Importance index. 5. Interpretation of the importance index. 6. Conclusion. ch. 16. A fuzzy constraint-based approach to the analytic hierarchy process. 1. Introduction. 2. Earlier works. 3. An approach using a fuzzy-valued reciprocal matrix. 4. An example. 5. Evaluating decisions. 6. Conclusions. ch. 17. Using different transitivity properties to deal with incomplete fuzzy preference relations in group decision making environments. 1. Introduction. 2. Preliminaries. 3. Consistency measures based on different transitivity properties. 4. Generalized procedure to estimate missing values. 5. Conclusions and further works. ch. 18. A bargaining agent models its opponent with entropy-based inference. 1. Introduction. 2. The negotiating agent: NA. 3. Estimating P(OPAcc(.)). 4. Estimating P(NAAcc(.)). 5. Negotiation strategies. 6. Conclusions. ch. 19. Comparison of spatiotemporal difference of brain activity between correct and approximation answer choices on addition. 1. Introduction. 2. Experiments. 3. Experimental results. 4. Discussion. ch. 20. Overabundant answers to flexible queries - a proximity-based intensification approach. 1. Introduction. 2. Background. 3. Overabundant answers. 4. Comparison with other modifier-based approaches. 5. Case of conjunctive flexible queries. 6. Conclusion -- Systems modeling and applications. ch. 21. 7. Numerical stability and further benchmarking. 8. Conclusions. ch. 35. Topological relations on fuzzy regions - an extended application of intersection matrices. 1. Introduction. 2. Regions with undetermined boundaries. 3. Topological relations. 4. Defining the fuzzy region topology. 5. Conclusion. ch. 36. Quantifier elimination versus generalized interval evaluation - a comparison on a specific class of quantified constraints. 1. Introduction. 2. Problem statement. 3. A specific quantifier elimination. 4. Generalized interval evaluation. 5. Comparison of the two methods. 6. Conclusion. |