Observational Learning Algorithm for an Ensemble of Neural Networks

Min Jang; Sungzoon Cho

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Observational Learning Algorithm for an Ensemble of Neural Networks

机译：神经网络集成的观测学习算法

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摘要

We propose Observational learning Algorithm (OLA), an ensemble learning algorithm with T and O steps alternating. In the T-step, an ensemble of networks is trained with a training data set. In the O-step, 'virtual' data are generated in which each target pattern Is determined by observing the member networks' output for the input pattern. These virtual data are added to the training data and the Two steps are repeatedly executed. The virtual data was found to play the role of a regularisation term as well as that of temporary hints Having the auxiliary information regarding the target function extracted for the ensemble.

机译：我们提出了观测学习算法（OLA），这是一种T和O步交替进行的集成学习算法。在T步中，使用训练数据集训练一组网络。在O步中，生成“虚拟”数据，其中通过观察成员网络针对输入模式的输出来确定每个目标模式。将这些虚拟数据添加到训练数据中，然后重复执行两个步骤。发现虚拟数据不仅充当正则化术语，而且还充当临时提示的角色，这些临时提示具有有关为集合提取的目标函数的辅助信息。

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《Pattern Analysis and Applications》 |2002年第2期|p.154-167|共14页
作者
Min Jang; Sungzoon Cho;
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作者单位

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收录信息美国《科学引文索引》(SCI);
原文格式 PDF
正文语种 eng
中图分类计算技术、计算机技术;
关键词
Neural network ensemble; Observational learning; Social learning theory; Virtual data;

机译：神经网络集成;观察学习;社会学习理论;虚拟数据;

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Observational Learning Algorithm for an Ensemble of Neural Networks

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