Mixing Aveboost and Conserboost to Improve Boosting Methods

机译：混合Aveboost和Conserboost以改进增强方法

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Adaptive boosting (Adaboost) is one of the most known methods to build an ensemble of neural networks. Adaboost has been studied and successfully improved by some authors like Breiman, Kuncheva or Oza. In this paper we briefly analyze and mix two of the m

机译：自适应增强（Adaboost）是构建神经网络集成的最著名方法之一。 Breaboman，Kuncheva或Oza等一些作者已经研究并成功改进了Adaboost。在本文中，我们简要分析并混合了两个

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《Neural Networks, The 2007 IEEE International Joint Conference on》|2007年|P.672-677|共6页
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Torres-Sospedra; J.; Hernandez-Espinosa; C.; Fernandez-Redondo; M.;
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中图分类工业技术;
关键词
learning (artificial intelligence); sampling methods; statistical distributions; Adaboost sampling distribution; Aveboost method; Conserboost method; adaptive boosting; averaged conservative boosting; conservative boosting; conservative equation; neural network en;

机译：学习（人工智能）;抽样方法;统计分布; Adaboost抽样分布; Aveboost方法; Conserboost方法;自适应提升;平均保守提升;保守提升;保守方程;神经网络;

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4. Mixing Aveboost and Conserboost to Improve Boosting Methods [C] . Torres-Sospedra, J., Hernandez-Espinosa, Neural Networks, The 2007 IEEE International Joint Conference on . 2007

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Mixing Aveboost and Conserboost to Improve Boosting Methods

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