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Textual case-based reasoning

The Knowledge Engineering Review, 2005
This commentary provides a definition of textual case-based reasoning (TCBR) and surveys research contributions according to four research questions. We also describe how TCBR can be distinguished from text mining and information retrieval. We conclude with potential directions for TCBR research.
Rosina O. Weber   +2 more
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Hybrid case-based reasoning

The Knowledge Engineering Review, 1994
Abstract This paper reviews a number of hybrid Case-Based Reasoning (CBR) systems. These systems are hybrid because the CBR components cooperate with one or more “co-reasoners” which employ a different type of reasoning strategy (e.g. qualitative simulation, constraint satisfaction, etc.). In this paper, we propose that CBR is in fact
John Hunt, Roger Miles
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Case-Based Reasoning with Confidence

2000
A case-based reasoning system can produce both a solution and an estimate of the confidence in that solution. The confidence value can be used to determine whether the solution does or does not have the needed accuracy. A statistical method can be used to compute a confidence value from information generated during the case-based reasoning process ...
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Reformulation in case-based reasoning

1998
By generalising our common experience, this paper addresses case-based reasoning that employs reformulations. Reformulation is useful when standard mapping is insufficient to retrieve a case. The paper provides a definition of reformulation and shows how reformulation is linked to retrieval and adaptation in the case-based reasoning cycle.
Erica Melis, Jean Lieber, Amedeo Napoli
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CASE-BASED-REASONING FOR IMAGE SEGMENTATION

International Journal of Pattern Recognition and Artificial Intelligence, 2008
This paper proposes to use case-based-reasoning for grey-level image segmentation. Different approaches to image segmentation have been proposed in the literature. The selection of the segmentation approach and the assignment of the values to the parameters involved in the selected algorithm depend on image domain and on the specific application.
Maria Frucci   +2 more
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An Approach for Temporal Case-Based Reasoning: Episode-Based Reasoning

2005
In recent years, several researchers have studied the suitability of CBR to cope with dynamic or continuous or temporal domains. In these domains, the current state depends on the past temporal states. This feature really makes difficult to cope with these domains. This means that classical individual case retrieval is not very accurate, as the dynamic
Miquel Sànchez-Marrè   +4 more
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Case-based reasoning: An overview

AI Commun., 1997
This paper contains a brief overview of Case-Based Reasoning (CBR) with an emphasis on European activities in the field. The main objective was to have a balance between brevity and expressiveness and providing helpful pointers to the field. It identifies major open problems of CBR associated with: retrieval/selection, memory organization, matching ...
López de Mántaras, Ramón   +1 more
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Reflective Reasoning in a Case-Based Reasoning Agent

1999
As a Case-Based Reasoning agent (CBR) evolves over time, and solves new problems based on previous experiences, there are some pitfalls that can appear in the problem-solving task. When those troubles arise, is the time to start some reflective reasoning tasks to overcome those problems and to improve the CBR performance.
Miquel Sànchez-Marrè   +4 more
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Case-Based Reasoning for Financial Prediction

2005
A concept of financial prediction system is considered in this paper. By integrating multidimensional data technology (data warehouse, OLAP) and case-based reasoning, we are able to predict financial trends and provide enough data for business decision making.
Simić, Dragan   +3 more
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Case-Based Reasoning and the Statistical Challenges

Quality and Reliability Engineering International, 2008
AbstractCase‐based reasoning (CBR) solves problems using the already stored knowledge and captures new knowledge, making it immediately available for solving the next problem. Therefore, CBR can be seen as a method for problem solving and also as a method to capture new experience and make it immediately available for problem solving.
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