Format:
1 Online-Ressource (105 Seiten)
Edition:
Reproduktion Electronic reproduction; Available via World Wide Web
ISBN:
9781627050081
Series Statement:
Synthesis Lectures on Artificial Intelligence and Machine Learning #20
Content:
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
Content:
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
Content:
1. Introduction -- 1.1 CBR systems taxonomy -- 1.2 Foundational issues -- 1.3 Related fields -- 1.4 Bibliographic notes --
Content:
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 --
Content:
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 --
Content:
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 --
Content:
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 --
Content:
6. Summary and beyond -- 6.1 Explanations -- 6.2 Provenance -- 6.3 Distributed approaches -- 6.4 Bibliographic notes -- Bibliography -- Author's biography
Note:
Description based upon print version of record
,
Preface; Acknowledgments; Introduction; CBR Systems Taxonomy; Foundational Issues; Related Fields; Bibliographic Notes; The Case-Base; Vocabulary; Case Modeling; Problem Description; Solution Description; Outcome; Case-Base Organization; Bibliographic Notes; Reasoning and Decision Making; Retrieve; Similarity Assessment; Ranking and Selection; Normalization, Discretization, and Missing Data; Reuse; Solution Copy; Solution Adaptation; Specific Purpose Methods; Revise; Bibliographic Notes; Learning; Similarity Learning; Measure Learning; Feature Relevance Learning; Maintenance; Retain; Review
,
RestoreBibliographic Notes; Formal Aspects; Description Logics; Bayesian Model; Fuzzy Set Formalization; Probabilistic Formalization; Case-Based Decisions; Bibliographic Notes; Summary and Beyond; Explanations; Provenance; Distributed Approaches; Bibliographic Notes; Bibliography; Author's Biography
,
Electronic reproduction; Available via World Wide Web
,
Mode of access: World Wide Web.
,
System requirements: Adobe Acrobat Reader.
Additional Edition:
ISBN 9781627050074
Additional Edition:
Erscheint auch als Druck-Ausgabe Case-Based Reasoning A Concise Introduction
Language:
English
Keywords:
Electronic books
DOI:
10.2200/S00490ED1V01Y201303AIM020
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