Machine learningGenerative / pretraining
深度信念网络 (DBN)
深度信念网络是一种生成概率模型，由多层随机的隐变量组成。DBN 由 Hinton、Osindero 和 Teh 于 2006 年提出，是最早能够被有效训练的深度架构之一。相邻的层对构成一个受限玻尔兹曼机 (Restricted Boltzmann Machine, RBM)，网络逐层贪婪训练，然后再进行可选的监督微调。DBN 重新激发了人们对深度学习的兴趣，并证明了从原始数据中进行分层特征学习是可行的。
阅读完整方法
仅限会员
登录使用免费账户登录即可阅读本节。
Method map
The neighbourhood of related methods — select a node to explore.
来源
- Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554. DOI: 10.1162/neco.2006.18.7.1527 ↗
如何引用本页
ScholarGate. (2026, June 2). Deep Belief Network (DBN). ScholarGate. https://scholargate.app/zh/deep-learning/deep-belief-network
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
Compare side by side →