Learning Coefficient in Bayesian Estimation of Restricted Boltzmann Machine
Learning Coefficient in Bayesian Estimation of Restricted Boltzmann Machine
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DOI：
10.18409/jas.v4i1.18
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发表时间：
2013-04
期刊：
影响因子：
--
通讯作者：
Miki Aoyagi
中科院分区：
文献类型：
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作者：
Miki Aoyagi
在贝叶斯估计中，我们考虑了学习模型的真实的对数典型阈值。该阈值对应于贝叶斯估计中的泛化误差的学习系数，其用于测量分层学习模型中的学习效率[30，31，33]。本文阐明了限制玻尔兹曼机的对数正则阈值的理想，并考虑了该模型的学习系数。
We consider the real log canonical threshold for the learning model in Bayesian estimation. This threshold corresponds to a learning coefficient of generalization error in Bayesian estimation, which serves to measure learning efficiency in hierarchical learning models [30, 31, 33]. In this paper, we clarify the ideal which gives the log canonical threshold of the restricted Boltzmann machine and consider the learning coefficients of this model.