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- W2040303975 abstract "We present a new co-training style framework and combine it with ensemble learning to further improve the generalization ability. By employing different strategies to combine co-training with ensemble learning, two learning algorithms, Sequential Ensemble Co-Learning (SECL) and Parallel Ensemble Co-Learning (PECL) are developed. Furthermore, we propose a weighted bagging method in PECL to generate an ensemble of diverse classifiers at the end of co-training. Finally, based on the voting margin, an upper bound on the generalization error of multi-classifier voting systems is given in the presence of both classification noise and distribution noise. Experimental results on six datasets show that our method performs better than other compared algorithms." @default.
- W2040303975 created "2016-06-24" @default.
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- W2040303975 date "2015-06-01" @default.
- W2040303975 modified "2023-09-27" @default.
- W2040303975 title "A Novel Diversity-Based Semi-Supervised Learning Framework with Related Theoretical Analysis" @default.
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- W2040303975 doi "https://doi.org/10.1142/s0218213015500116" @default.
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