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Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Wray Buntine,Marko Grobelnik,John Shawe-Taylor Conference proce

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樓主: Capricious
41#
發(fā)表于 2025-3-28 17:10:22 | 只看該作者
Hybrid Least-Squares Algorithms for Approximate Policy Evaluation demonstrate their utility on a simple problem. Experimental results on both small and large domains suggest hybrid algorithms may find solutions that lead to better policies when performing policy iteration.
42#
發(fā)表于 2025-3-28 20:37:51 | 只看該作者
Learning Multi-linear Representations of Distributions for Efficient Inferencerithms for exact and approximate learning for MLR and, through a comparison with Bayes Net representations, demonstrate experimentally that MLR representations provide faster inference without sacrificing inference accuracy.
43#
發(fā)表于 2025-3-29 01:29:38 | 只看該作者
44#
發(fā)表于 2025-3-29 04:55:01 | 只看該作者
45#
發(fā)表于 2025-3-29 08:21:03 | 只看該作者
46#
發(fā)表于 2025-3-29 13:50:40 | 只看該作者
47#
發(fā)表于 2025-3-29 17:28:23 | 只看該作者
48#
發(fā)表于 2025-3-29 20:30:34 | 只看該作者
A Fast Ensemble Pruning Algorithm Based on Pattern Mining Processzes, the one with the best accuracy is outputted. Compared with Bagging, GASEN, and Forward Selection, experimental results show that PMEP achieves the best prediction accuracy and keeps the size of the final ensemble small, more importantly, its pruning time is much less than other ensemble pruning algorithms.
49#
發(fā)表于 2025-3-30 00:38:07 | 只看該作者
50#
發(fā)表于 2025-3-30 06:43:53 | 只看該作者
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