D. W. Aha and D Wettschereck. Case-based learning: Beyond classification of feature vectors. In Proceedings of the 9th European Conference on Machine Learning (ECML'97). Springer, 1997. Home/Search Document Details and Download
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Comparing Structures using a Hopfield-style Neural Network - Schädler, Wysotzki (Correct)
....However, many algorithms require the knowledge to be represented in a framework with a restricted representational expressiveness for reasons of complexity and consistency. While in Machine Learning feature vector representations as well as relational descriptions have been investigated (c.f. [1, 2] for a discussion of relational learning) in Knowledge Discovery, the higher complexity of processing relational descriptions has lead to an emphasis on attribute value representations. Methods comprising relational, numerical and other knowledge are under development in both domains today. Often ....
....Moreover, Tritop s decision tree classifier can be replaced or extended by a prototype classifier. 3.2. Supporting Case Based Reasoning The definition and computation of distances and mappings between relational objects has been a key issue in recent research on case based reasoning, c.f. [2, 12, 78, 79, 80]. MatchBox has been also used to support case based reasoning for programming by analogy. In [81] the comparison of small recursive programs by using the MatchBox approach is described. It is shown that knowledge about recursive programs can be incorporated in the matching procedure to achieve ....
David W. Aha and Dietrich Wettschereck. Casebased learning: Beyond classification of feature vectors. In Proc. of the ECML-97 Workshop, 1997.
A Case Study of Case-Based CBR - Leake, Kinley, Wilson (4 citations) (Correct)
....as cases, similarity criteria, and case adaptation information facilitates the development of CBR systems by enabling system developers to place knowledge in whichever container is most convenient. In addition, these multiple knowledge sources provide many opportunities for learning (e.g. Aha Wettschereck, 1997). Investigators have studied a range of analytic and inductive learning methods for refining the knowledge sources within CBR. For example, Hammond s (1989) This work was supported in part by the National Science Foundation under Grant No. IRI 9409348. CHEF uses explanation based methods to ....
D. Aha and D. Wettschereck. Case-based learning: Beyond classification of feature vectors. Call for papers of ECML-97 workshop, 1997.
NaCoDAE: Navy Conversational Decision Aids Environment - Breslow, Aha (1998) (1 citation) Self-citation (Aha) (Correct)
....in Case Based Reasoning that is funded by the Office of Naval Research. This project involves the creation of decision aids software development tools for Navy and DoD personnel that are based on case based reasoning (CBR) technology. CBR is a problem solving methodology (Aamodt Plaza, 1994; Aha Wettschereck, 1997; de Mantaras Plaza, 1997; Watson, 1997) that has recently gained great popularity in both the research and applied AI communities. For example, DARPA sponsored three CBR workshops in 1988, 1989, and 1991, while AAAI and MLNet have sponsored several CBR meetings since 1988 (e.g. three have been ....
....guidelines for authoring cases, these guidelines are numerous, often conflict, and require substantial time to master. Therefore, Code 4010 subsequently hired an expensive Inference Corporation consultant to re design their case library, which greatly improved their application s performance. We (Aha, 1997; Aha Breslow, 1997a; 1997b) have since implemented NaCoDAE, a CCBR system that mimics Inference s tools, and extended it with software that intelligently supports the case authoring process. In particular, one component of NaCoDAE, named Clire (Case LIbrary REvisor) automatically revises case ....
Aha, D. W., & Wettschereck, D. (1997). Case-based learning: Beyond classification of feature vectors. Proceedings of the Ninth European Conference on Machine Learning (pp. 329--336).
Learning Feature Weights - From Case Order (2001) (Correct)
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D. W. Aha and D Wettschereck. Case-based learning: Beyond classification of feature vectors. In Proceedings of the 9th European Conference on Machine Learning (ECML'97). Springer, 1997.
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