Titlebook: Case-Based Reasoning; A Concise Introducti Beatriz López Book 2013 Springer Nature Switzerland AG 2013

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书目名称Case-Based Reasoning影响因子(影响力)




书目名称Case-Based Reasoning影响因子(影响力)学科排名




书目名称Case-Based Reasoning网络公开度




书目名称Case-Based Reasoning网络公开度学科排名




书目名称Case-Based Reasoning被引频次




书目名称Case-Based Reasoning被引频次学科排名




书目名称Case-Based Reasoning年度引用




书目名称Case-Based Reasoning年度引用学科排名




书目名称Case-Based Reasoning读者反馈




书目名称Case-Based Reasoning读者反馈学科排名

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Nonflammable

Learning,, no learning effort is dedicated when storing cases in memory, but is at problem-solving time, practical CBR requires the use of more eager methods to improve efficiency in problem solving. Learning can be applied to obtain case models, as prototypes, to recognize the relevant features of the domai

NICE

Formal Aspects,systems. The behavior of a system should be predictable, thus the formal specification and then verification of CBR systems should be possible. CBR, as a methodology, encompasses several stages, while formal approaches used to cover part of them. For example, description logic focuses on knowledge r

BRINK

Summary and Beyond,ilar problems. Past experiences or cases are stored by keeping all the particular information of a problem-solving past episode, without dropping any particularities out, the removal of which could lead to the generation of general patterns. The experiences are, by themselves, part of the domain mod

harrow

harrow

Formal Aspects,ery well-known formalization approaches to CBR are provided, sorted by an approximate chronological order. It is beyond the scope of this introductory lecture to provide long debates and demonstrate CBR properties derived from the models, but the summaries which follow should be taken as the tools offered by the mathematical foundations of CBR.

vector

Learning,n or the problem at hand to learn the corresponding similarity measure, or to improve retrieval through a better case organization. That is, learning can be applied to any knowledge involved in the CBR, i.e., the knowledge containers.

MIRE

Summary and Beyond,el. When solving a problem, reasoning is carried out thanks to a neighborhood generalization procedure guided bya similarity measure. Thus, lazy learning is postponed until problem solving, and only part of the cases, those that are relevant to the problem at hand, participate in that process.

Bone-Scan

Operations Research in the Airline Industryn or the problem at hand to learn the corresponding similarity measure, or to improve retrieval through a better case organization. That is, learning can be applied to any knowledge involved in the CBR, i.e., the knowledge containers.