Optimal model inference for Bayesian mixture of experts

被引：0
|
作者：
Ueda, N [1 ]
Ghahramani, Z [1 ]
机构：
[1] NTT, Commun Sci Labs, Kyoto 6190237, Japan
来源：
NEURAL NETWORKS FOR SIGNAL PROCESSING X, VOLS 1 AND 2, PROCEEDINGS | 2000年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We present an algorithm for inferring the parameter and model structure of a mixture of experts model (MoE) based on the variational Bayesian (VB) framework. First, in the VB framework, we show that the model parameter and structure of a MoE can be simultaneously optimized by maximizing an objective funtion derived in this paper. Next, we present a deterministic algorithm to find the optimal number of experts of a MoE while avoiding local maxima. Our experimental results demonstrate the practical usefulness of the method.
引用
收藏
页码：145 / 154
页数：10
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