By Barry Smyth (auth.), Enrico Blanzieri, Luigi Portinale (eds.)
This publication constitutes the refereed court cases of the fifth ecu Workshop on Case-Based Reasonning, EWCBR 2000, held in Trento, Italy in September 2000. The forty revised complete papers provided including invited contributions have been conscientiously reviewed and chosen for inclusion within the ebook. All curves concerns in case-based reasoning, starting from foundational and theoretical elements to complex purposes in quite a few fields are addressed.
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Extra resources for Advances in Case-Based Reasoning: 5th European Workshop, EWCBR 2000 Trento, Italy, September 6–9, 2000 Proceedings
This approach proceeds by learning a theory containing context free tagging rules in the first iteration and then by learning context dependent recursive theories in subsequent iterations until all words can be tagged. The training data employed for each iteration consists of the words that could not be tagged in previous iterations. Despite the high predictive ability of the iterative tagging approach, it was clear that the theory produced in the last iteration was responsible for a large number of errors that would have a large impact in the overall result.
25 0 0 25 50 Similarity 75 Figure 12: Individual Confidence Measure for Similarity Case-Based Reasoning with Confidence 25 The individual confidence measure for similarity had good confidence in 94% of the subjects that the expert considered good. This confidence measure is now being used to determine if the color match suggested by the case-based reasoning system should be used or if a labor intensive process of color matching should be started. The expert is only needed for borderline cases. 5 Summary This paper showed that the confidence measures provided a dramatic increase in the usefulness of the two systems described.
1, (1994), 39-59. 2. : Morphosyntatic Tagging of Slovene Using Progol. Proceedings of the 9th Int. Workshop on Inductive Logic Programming (ILP-99). Dzeroski, S. and Flach, P. (Eds). LNAI 1634, 1999. 3. : Part of Speech Tagging Using Progol. In Inductive Logic Programming. Proceedings of the 7th Int. Workshop on Inductive Logic Programming (ILP-97). LNAI 1297, 1997. 4. : Unifying Instance-Based and Rule-Based Induction. Machine Learning 24 (1996), 141-168. 5. Golding, A. : Improving Accuracy by Providing Rule-based and Case-based Reasoning.