HIERARCHICAL MIXTURES OF EXPERTS AND THE EM ALGORITHM
HIERARCHICAL MIXTURES OF EXPERTS AND THE EM ALGORITHM
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DOI：
10.1162/neco.1994.6.2.181
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发表时间：
1994-03-01
影响因子：
2.9
通讯作者：
JACOBS, RA
中科院分区：
文献类型：
--
作者：
JORDAN, MI;JACOBS, RA
我们提出了一个监督学习的树结构体系结构。该体系结构的统计模型是一个分层混合模型，其中混合系数和混合成分都是广义线性模型（GLIM）。学习被视为一个最大似然问题，特别是，我们提出了一个期望最大化（EM）算法调整参数的架构。我们还开发了一个在线学习算法，其中的参数是增量更新。在机器人动力学域的比较仿真结果。
We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM's). Learning is treated as a maximum likelihood problem; in particular, we present an Expectation-Maximization (EM) algorithm for adjusting the parameters of the architecture. We also develop an on-line learning algorithm in which the parameters are updated incrementally. Comparative simulation results are presented in the robot dynamics domain.