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Titlebook: Case-Based Learning; Janet L. Kolodner Book 1993 Springer Science+Business Media New York 1993 Action.C programming language.case-based re

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發(fā)表于 2025-3-21 17:16:00 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Case-Based Learning
編輯Janet L. Kolodner
視頻videohttp://file.papertrans.cn/223/222314/222314.mp4
圖書封面Titlebook: Case-Based Learning;  Janet L. Kolodner Book 1993 Springer Science+Business Media New York 1993 Action.C programming language.case-based re
描述Case-based reasoning means reasoning based on rememberingprevious experiences. A reasoner using old experiences (cases) mightuse those cases to suggest solutions to problems, to point outpotential problems with a solution being computed, to interpret a newsituation and make predictions about what might happen, or to createarguments justifying some conclusion. A case-based reasoner solves newproblems by remembering old situations and adapting their solutions.It interprets new situations by remembering old similar situations andcomparing and contrasting the new one to old ones to see where it fitsbest. Case-based reasoning combines reasoning with learning. It spansthe whole reasoning cycle. A situation is experienced. Old situationsare used to understand it. Old situations are used to solve a problem(if there is one to be solved). Then the new situation is insertedinto memory alongside the cases it used for reasoning, to be usedanother time..The key to this reasoning method, then, is remembering. Rememberinghas two parts: integrating cases or experiences into memory when theyhappen and recalling them in appropriate situations later on. Thecase-based reasoning community calls this rel
出版日期Book 1993
關(guān)鍵詞Action; C programming language; case-based reasoning; learning; machine learning
版次1
doihttps://doi.org/10.1007/978-1-4615-3228-6
isbn_softcover978-1-4613-6418-4
isbn_ebook978-1-4615-3228-6
copyrightSpringer Science+Business Media New York 1993
The information of publication is updating

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發(fā)表于 2025-3-21 20:38:49 | 只看該作者
Derivational Analogy in PRODIGY: Automating Case Acquisition, Storage, and Utilization, in some detail, and extensive results of the first full implementation are presented. These results show up to a large performance improvement in a simple transportation domain for structurally similar problems, and smaller improvements when less strict similarity metrics are used for problems that
板凳
發(fā)表于 2025-3-22 03:20:04 | 只看該作者
Opportunism and Learning,lishes this by treating the behavior of intelligent agents as an ongoing attempt to discover, create, and maintain the stability that is necessary for the production of actions that satisfy our goals.
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發(fā)表于 2025-3-22 07:47:07 | 只看該作者
https://doi.org/10.1007/978-1-4615-5021-1owledge in response to experiences in such situations. The theory has been implemented in a case-based story understanding program that can (a) learn a new case in situations where no case already exists, (b) learn how to index the case in memory, and (c) incrementally refine its understanding of th
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ngside the cases it used for reasoning, to be usedanother time..The key to this reasoning method, then, is remembering. Rememberinghas two parts: integrating cases or experiences into memory when theyhappen and recalling them in appropriate situations later on. Thecase-based reasoning community calls this rel978-1-4613-6418-4978-1-4615-3228-6
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發(fā)表于 2025-3-23 03:16:55 | 只看該作者
Book 1993t solutions to problems, to point outpotential problems with a solution being computed, to interpret a newsituation and make predictions about what might happen, or to createarguments justifying some conclusion. A case-based reasoner solves newproblems by remembering old situations and adapting thei
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Integrating Feature Extraction and Memory Search,make an abstract thematic feature valuable to a case-based reasoner, and recasts the problem of case retrieval into a framework under which a system can explicitly and dynamically reason about the cost of acquiring features relative to their information value.
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