A. Stahl. Learning feature weights from case order feedback. In Proceedings of the 4th International Conference on Case-Based Reasoning. Springer, 2001. Home/Search Document Not in Database Summary Related Articles Check |
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A CBR Approach to the Heterogeneous Players Problem in Robotic.. - Gabel (2002) (Correct)
....into categories with a width of 0.07 for the purpose of better illustration. With di erent shades of gray we emphasize which evaluation values assigned by e 2 correspond to values that are assigned to a certain match by e. 27 Case Retrieval: Adjusting the Similarity Measures According to [ 16 ] de ning adequate similarity measures is one of the most important tasks when implementing case based applications. For this reason it is important to pay special attention to the development of a similarity metric between cases in the considered soccer domain, too. In this section we introduce ....
....out of the player type pool we ran a soccer match using the Soccer Server, analyzed the resulting log le, extracted the important information, packed them into a case and stored that case to the training data case base . 5.4 Similarity Teacher and Learner 5.4. 1 The Learning Framework In [ 16 ] a framework for learning similarity measures is presented. It makes use of case order feedback and an abstract concept, the so called similarity teacher in order to realize the learning. We have adopted that approach, modi ed it to our needs, and applied it to learn the feature weights of the ....
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A. Stahl. Learning Feature Weights from Case Order Feedback. Proceedings of the 4th International Conference on Case-Based Reasoning (ICCBR) , Vancouver, 2001.
Optimizing Return-Set Size for Requirements Satisfaction and.. - Branting (2002) (Correct)
....requirements. An item in the inventory that satisfies the customer s requirements at least as well as any other item in the inventory is optimal with respect to the inventory and requirements. Case based reasoning (CBR) is an increasingly popular paradigm for the inventory selection task [KSB01, Sta01, Wil99] In contrast to standard database retrieval, which is restricted to exact matches, retrieval in CBR systems can involve partial matches ordered by the degree to which each product satisfies the customer s requirements. This permits an optimal item to be presented to the customer even when ....
....values, and LCWwas applied to each simulated customer in turn. Fifty rounds were performed for each inventory, set of customers, s, maximum retrievals, and return set size. Figure 1 displays the mean cosine between the vectors representing the actual and hypothesized global weights as See [Sta01] for an approach to feature weight optimization based on gradient ascent. 0 1 Figure 1. The mean cosine between the actual and hypothesized global weights as a function of the number of customers seen, for s from 0 to 1.0. a function of the number of customers seen. Separate learning ....
A. Stahl. Learning feature weights from case order feedback. In D. Aha and I. Watson, editors, Case-Based Reasoning Research and Development, 4th International Conference on Case-Based Reasoning, ICCBR 2001, 6 Lecture Notes in Artificial Intelligence 2080, pages 502--516, Vancouver, BC, Canada, 2001. Springer.
Selecting Heterogeneous Team Players by Case-Based Reasoning.. - Gabel, Veloso (2001) (Correct)
....a certain match by e. 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 e 2 Figure 9: Comparison between the score based evaluation function e 2 and the newly introduced evaluation function e 5 Case Retrieval: Adjusting the Similarity Measures 5. 1 Basic Similarity Measures According to [ 20 ] de ning adequate similarity measures is one of the most important tasks when implementing case based applications. In this section we introduce similarity measures between heterogeneous player types between cases of application of heterogeneous player types The latter similarity measure ....
....out of the player type pool we ran a soccer match using the Soccer Server, analyzed the resulting log le, extracted the important information, packed them into a case and stored that case to the training data case base . 5.3 Similarity Teacher and Learner 5.3. 1 The Learning Framework In [ 20 ] a framework for learning similarity measures is presented. It makes use of case order feedback and an abstract concept, the so called similarity teacher in order to realize the learning. We have adopted that approach, modi ed it to our needs, and applied it to learn the feature weights of the ....
[Article contains additional citation context not shown here]
A. Stahl. Learning Feature Weights from Case Order Feedback. Proceedings of the 4th International Conference on Case-Based Reasoning (ICCBR) , Vancouver, 2001.
Defining Similarity Measures: - Top-Down Vs Bottom-Up Self-citation (Stahl) (Correct)
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A. Stahl. Learning feature weights from case order feedback. In Proceedings of the 4th International Conference on Case-Based Reasoning. Springer, 2001.
Using Evolution Programs to Learn - Local Similarity Measures (2003) Self-citation (Stahl) (Correct)
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A. Stahl. Learning feature weights from case order feedback. In Proceedings of the 4th International Conference on Case-Based Reasoning. Springer, 2001.
Optimizing Retrieval in CBR - Introducing Solution Similarity Self-citation (Stahl) (Correct)
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A. Stahl. Learning feature weights from case order feedback. In Proceedings of the 4th International Conference on CaseBased Reasoning (ICCBR 2001.
Using a Relevance Model for Performing Feature Weighting - Merida-Campos, Rollon (Correct)
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A.Stahl. Learning Feature Weights from Case Order Feedback. Eds.D.W.Aha and I.Watson: ICCBR 01, LNAI 2080.
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