Abstract
Most machine learning algorithms are eager methods in the sense that a model is generated with the complete training data set and, afterwards, this model is used to generalize the new test instances. In this work we study the performance of different machine learning algorithms when they are learned using a lazy approach. The idea is to build a classification model once the test instance is received and this model will only learn a selection of training patterns, the most relevant for the test instance. The method presented here incorporates a dynamic selection of training patterns using a weighting function. The lazy approach is applied to machine learning algorithms based on different paradigms and is validated in different classification domains.
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© 2009 IFIP International Federation for Information Processing
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Galván, I.M., Valls, J.M., Lecomte, N., Isasi, P. (2009). A Lazy Approach for Machine Learning Algorithms. In: Iliadis, Maglogiann, Tsoumakasis, Vlahavas, Bramer (eds) Artificial Intelligence Applications and Innovations III. AIAI 2009. IFIP International Federation for Information Processing, vol 296. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-0221-4_60
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DOI: https://doi.org/10.1007/978-1-4419-0221-4_60
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-0220-7
Online ISBN: 978-1-4419-0221-4
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