Case-Based Approximate Reasoning: 44 - Brossura

Hüllermeier, Eyke

Brossura

ISBN 10: 9048174317 ISBN 13: 9789048174317

Casa editrice: Springer, 2013

Riassunto

Making use of different frameworks of approximate reasoning and reasoning under uncertainty, notably probabilistic and fuzzy set-based techniques, this book develops formal models of the above inference principle, which is fundamental to CBR. The case-based approximate reasoning methods thus obtained especially emphasize the heuristic nature of case-based inference and aspects of uncertainty in CBR.

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Recensione

From the reviews:

"In the last years developments were very successful that have been based on the general concept of case-based reasoning. ... will get a lot of attention and for a good while will be the reference for many applications and further research. ... the book can be used as an excellent guideline for the implementation of problem-solving programs, but also for courses in Artificial and Computional Intelligence. Everybody who is involved in research, development and teaching in Artificial Intelligence will get something out of it." (Christian Posthoff, Zentralblatt MATH, Vol. 1119 (21), 2007)

Contenuti

Notation.- 1. Introduction.1.1 Similarity and case-based reasoning.1.2 Objective of this book. 1.3 Overview.- 2. Similarity and Case-Based Inference. 2.1 Model-based and instance-based approaches. 2.2 Similarity-based methods. 2.4 Case-based inference. 2.5 Summary and remarks.- 3. Constraint-Based Modeling of Case-Based Inference. 3.1 Basic concepts. 3.2 Constraint-based inference. 3.3 Case-based approximation. 3.4 Learning similarity hypotheses. 3.5 Application to statistical inference. 3.6 Summary and remarks.- 4. Probabilistic Modeling of Case-Based Inference. 4.1 Basic probabilistic concepts. 4.2 Case-based inference, probabilistic reasoning, and statistical inference. 4.3 Learning probabilistic similarity hypotheses. 4.4 Experiments with regression and label ranking. 4.5 Case-based inference as evidential reasoning. 4.6 Assessment of cases. 4.7 Complex similarity hypotheses. 4.8 Approximate probabilistic inference. 4.9 Summary and remarks.- 5. Fuzzy Set-Based Modeling of Case-Based Inference I. 5.1 Background on possibility theory . 5.2 Fuzzy rule-based modeling of the CBI hypothesis. 5.3 Generalized possibilistic. 5.4 Extensions of the basic model. 5.5 Experimental studies. 5.6 Calibration of CBI models. 5.7 Relations to other fields. 5.8 Summary and remarks. 6.1 Gradual inference rules. 6.2 Certainty rules. 6.3 Cases as information sources. 6.4 Exceptionality and assessment of cases. 6.5 Local rules. 6.6 Summary and remarks.- 7. Case-Based Decision Making. 7.1 Case-based decision theory. 7.2 Nearest Neighbor decisions. 7.4 Fuzzy quantification in act evaluation. 7.5 A CBI framework of CBDM. 7.6 CBDM models: A discussion of selected issues. 7.7 Experience-based decision making. 7.8 Summary and remarks.- 8. Conclusions and Outlook A. Possibilistic Dominance in Qualitative Decisions.- References.

Dettagli dell?edizione

• Editore: Springer
• Data di pubblicazione: 2013
• ISBN 10: 9048174317
• ISBN 13: 9789048174317
• Rilegatura: Copertina flessibile
• Lingua: Inglese
• Numero di pagine: 388