Case-Based Approximate Reasoning

Author:   Eyke Hüllermeier
Publisher:   Springer-Verlag New York Inc.
Edition:   2007 ed.
Volume:   44
ISBN:  

9781402056949


Pages:   372
Publication Date:   23 January 2007
Format:   Hardback
Availability:   In Print   Availability explained
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Case-Based Approximate Reasoning


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Overview

Case-based reasoning (CBR) has received a great deal of attention in recent years and has established itself as a core methodology in the field of artificial intelligence. The key idea of CBR is to tackle new problems by referring to similar problems that have already been solved in the past. More precisely, CBR proceeds from individual experiences in the form of cases. The generalization beyond these experiences typically relies on a kind of regularity assumption demanding that 'similar problems have similar solutions'. 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. This way, the book contributes to a solid foundation of CBR which is grounded on formal concepts and techniques from the aforementioned fields. Besides, it establishes interesting relationships between CBR and approximate reasoning, which not only cast new light on existing methods but also enhance the development of novel approaches and hybrid systems. This books is suitable for researchers and practioners in the fields of artifical intelligence, knowledge engineering and knowledge-based systems.

Full Product Details

Author:   Eyke Hüllermeier
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   2007 ed.
Volume:   44
Dimensions:   Width: 15.50cm , Height: 2.20cm , Length: 23.50cm
Weight:   1.590kg
ISBN:  

9781402056949


ISBN 10:   140205694
Pages:   372
Publication Date:   23 January 2007
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

Similarity and Case-Based Inference.- Constraint-Based Modeling of Case-Based Inference.- Probabilistic Modeling of Case-Based Inference.- Fuzzy Set-Based Modeling of Case-Based Inference I.- Fuzzy Set-Based Modeling of Case-Based Inference II.- Case-Based Decision Making.- Conclusions and Outlook.

Reviews

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)


From the reviews: <p> In the last years developments were very successful that have been based on the general concept of case-based reasoning. a ] will get a lot of attention and for a good while will be the reference for many applications and further research. a ] 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)


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