D. Wettschereck, D.W. Aha, and T. Mohri, `A review and empirical comparison of feature weighting methods for a class of lazy learning algorithms', AI Review, 11, 273--314, (1997).  Home/Search Document Not in Database Summary Related Articles Check |
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Probability Based Metrics for Nearest Neighbor.. - Blanzieri, Ricci (1999) (1 citation) (Correct)
....is straightforward: given a set of classified examples, which are described as points in an input space, a new unclassified example is assigned to the known class of the nearest example. The nearest relation is computed using a (similarity) metric defined on the input space. Many researchers [21 23, 11, 1, 2, 14, 13, 18, 19, 7, 25] focused their attention on the use of local metrics, i.e. metrics that vary depending on the position of the points in the input space. Conversely, more traditional global metrics assume that similarity evaluation should be independent from the area of the input space the cases to be compared are ....
D. Wettschereck, T. Mohri, and D. W. Aha. A review and empirical comparison of feature weighting methods for a class of lazy learning algorithms. AI Review Journal, 11:273--314, 1997.
Supporting Conversational Case-Based Reasoning in an.. - Aha, Breslow, Maney (1998) (4 citations) Self-citation (Aha) (Correct)
....useful for CCBR tools. For example, hq,ai pairs in case states are often annotated with weights, similar to feature weights, so as to bias the similarity function. A machine learning approach could be used to automatically tune these weights, as has been done for classification and planning tasks (Wettschereck et al. 1997; Mu noz Avila Hullen, 1996) This would further simplify the case authoring task, allowing users to ignore the challenging problem of assigning weights manually. Conversational Case Based Reasoner Rule Generator S E R U Interactions Case Library Implied Answers Queries Parka DB Parka DB ....
Wettschereck, D., Aha, D. W. & Mohri, T. (1997). A review and empirical comparison of feature weighting methods for a class of lazy learning algorithms. Artificial Intelligence Review, 11(1--5), 273--314.
The Omnipresence of Case-Based Reasoning in Science and Application - Aha (1998) (5 citations) Self-citation (Aha) (Correct)
....implies both. 2 CNN was also rediscovered elsewhere. For example, see (Kurtzberg, 1987) ffl feature selection and weighting (Kelly Davis, 1991; Cain et al. 1991; Cardie, 1993; Skalak, 1994; Ricci Avesani, 1995; Kohavi et al. 1997; Domingos, 1997; Ling Wang, 1997; Maron Moore, 1997; Wettschereck et al. 1997; Howe Cardie, 1997) ffl information theory (Lee, 1994; Cleary Trigg, 1995; Wettschereck Dietterich, 1995) ffl noise (Stanfill, 1987; Aha Kibler, 1989; Aha et al. 1991; Ting, 1997) ffl parallel implementations (Stanfill Waltz, 1986) ffl preference learning (Branting Broos, ....
Wettschereck, D., Aha, D. W. & Mohri, T. (1997). A review and empirical comparison of feature weighting methods for a class of lazy learning algorithms. Artificial Intelligence Review, 11(1--5), 273--314.
Instance-Based Prediction with Guaranteed Confidence - Hüllermeier (2004) (Correct)
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D. Wettschereck, D.W. Aha, and T. Mohri, `A review and empirical comparison of feature weighting methods for a class of lazy learning algorithms', AI Review, 11, 273--314, (1997).
Possibilistic Instance-Based Learning - Hüllermeier (Correct)
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D. Wettschereck, D.W. Aha, and T. Mohri. A review and empirical comparison of feature weighting methods for a class of lazy learning algorithms. AI Review, 11:273--314, 1997.
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