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- W3148493224 abstract "1 Abstract: In this article, we introduce selective baggjng based incremental learning, an algorithm for incremental learning using selective ensemble. Selective bagging gains new information from the incremental data by selecting proper components. In the first situation of the incremental learning process, we train component predictors by bootstrap sampling nu the original data set, and then constitute the eosemble predictor by selecting proper component predictors based on a genetic algorithm. In the next situation, we re-select proper component predictors from the original component predictors on the incremental data set; or more new component predictors are trained on the incremental data set, and a new ensemble predictor is constituted by selecting so proper predictors from all component predictors on all validation data. The proposed algorithm enables the rerulting ensemble predictor to learn new informatinn from new data set without forgetting previously acquired knowledge. Enperiments on bandwritten digit recognition indicate that selective bagging based incremental learning is a promising learning algorithm." @default.
- W3148493224 created "2021-04-13" @default.
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- W3148493224 date "2004-01-01" @default.
- W3148493224 modified "2023-09-24" @default.
- W3148493224 title "SELECTnTE BAGGIING BASED INCREMENTAL LEARNING" @default.
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