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- W1834088915 abstract "In this paper we propose a fast and incremental algorithm for learning model trees from data streams (FIMT) for regression problems. The algorithm is incremental, works online, processes examples once at the speed they arrive, and maintains an any-time regression model. The leaves contain linear-models trained online from the examples that fall at that leaf, a process with low complexity. The use of linear models in the leaves increases its any-time global performance. FIMT is able to obtain competitive accuracy with batch learners even for medium size datasets, but with better training time in an order of magnitude. We study the properties of FIMT over several artificial and real datasets and evaluate its sensitivity on the order of examples and the noise level." @default.
- W1834088915 created "2016-06-24" @default.
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- W1834088915 date "2008-01-01" @default.
- W1834088915 modified "2023-09-25" @default.
- W1834088915 title "Learning Model Trees from Data Streams" @default.
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- W1834088915 doi "https://doi.org/10.1007/978-3-540-88411-8_8" @default.
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