منابع مشابه
Possibilistic instance-based learning
A method of instance-based learning is introduced which makes use of possibility theory and fuzzy sets. Particularly, a possibilistic version of the similarity-guided extrapolation principle underlying the instancebased learning paradigm is proposed. This version is compared to the commonly used probabilistic approach from a methodological point of view. Moreover, aspects of knowledge represent...
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Instance-based learning methods explicitly remem ber all the data that they receive They usually have no training phase and only at prediction time do they perform computation Then they take a query search the database for similar datapoints and build an on-line local model (such as a local average or local regression) with which to predict an output value In this paper we review the advantage...
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This paper proposes a new algorithm for acquisition of preference predicates by a learning apprentice, termed Compositional Instance-Based Learning (CIBL), that permits multiple instances of a preference predicate to be composed, directly exploiting the transitivity of preference predicates. In an empirical evaluation, CIBL was consistently more accurate than a I-NN instance-based learning stra...
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Traditional instance-based learning methods base their predictions directly on (training) data that has been stored in the memory. The predictions are based on weighting the contributions of the individual stored instances by a distance function implementing a domain-dependent similarity metrics. This basic approach suuers from three drawbacks: com-putationally expensive prediction when the dat...
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ژورنال
عنوان ژورنال: Artificial Intelligence
سال: 2003
ISSN: 0004-3702
DOI: 10.1016/s0004-3702(03)00019-5