Case-based reasoning : a concise introduction /
By: López, Beatriz.
Material type:
BookSeries: Synthesis digital library of engineering and computer science: ; Synthesis lectures on artificial intelligence and machine learning: # 20.Publisher: San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, c2013Description: 1 electronic text (xv, 87 p.) : ill., digital file.ISBN: 9781627050081 (electronic bk.).Subject(s): Case-based reasoning | knowledge-based systems | problem-solving | reasoning | machine learning | learning from experiences | knowledge reuseDDC classification: 006.3 Online resources: Abstract with links to resource | Abstract with links to full text Also available in print.| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
E books
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PK Kelkar Library, IIT Kanpur | Available | EBKE489 |
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
Part of: Synthesis digital library of engineering and computer science.
Series from website.
Includes bibliographical references (p. 69-85).
1. Introduction -- 1.1 CBR systems taxonomy -- 1.2 Foundational issues -- 1.3 Related fields -- 1.4 Bibliographic notes --
2. The case-base -- 2.1 Vocabulary -- 2.2 Case modeling -- 2.2.1 Problem description -- 2.2.2 Solution description -- 2.2.3 Outcome -- 2.3 Case-base organization -- 2.4 Bibliographic notes --
3. Reasoning and decision making -- 3.1 Retrieve -- 3.1.1 Similarity assessment -- 3.1.2 Ranking and selection -- 3.1.3 Normalization, discretization, and missing data -- 3.2 Reuse -- 3.2.1 Solution copy -- 3.2.2 Solution adaptation -- 3.2.3 Specific purpose methods -- 3.3 Revise -- 3.4 Bibliographic notes --
4. Learning -- 4.1 Similarity learning -- 4.1.1 Measure learning -- 4.1.2 Feature relevance learning -- 4.2 Maintenance -- 4.2.1 Retain -- 4.2.2 Review -- 4.2.3 Restore -- 4.3 Bibliographic notes --
5. Formal aspects -- 5.1 Description logics -- 5.2 Bayesian model -- 5.3 Fuzzy set formalization -- 5.4 Probabilistic formalization -- 5.5 Case-based decisions -- 5.6 Bibliographic notes --
6. Summary and beyond -- 6.1 Explanations -- 6.2 Provenance -- 6.3 Distributed approaches -- 6.4 Bibliographic notes -- Bibliography -- Author's biography.
Abstract freely available; full-text restricted to subscribers or individual document purchasers.
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Case-based reasoning is a methodology with a long tradition in artificial intelligence that brings together reasoning and machine learning techniques to solve problems based on past experiences or cases. Given a problem to be solved, reasoning involves the use of methods to retrieve similar past cases in order to reuse their solution for the problem at hand. Once the problem has been solved, learning methods can be applied to improve the knowledge based on past experiences. In spite of being a broad methodology applied in industry and services, case-based reasoning has often been forgotten in both artificial intelligence and machine learning books. The aim of this book is to present a concise introduction to case-based reasoning providing the essential building blocks for the designing of case-based reasoning systems, as well as to bring together the main research lines in this field to encourage students to solve current CBR challenges.
Also available in print.
Title from PDF t.p. (viewed on May 21, 2013).
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