Optimal model inference for Bayesian mixture of experts

被引：0
|
作者：
Ueda, Naonori [1 ]
Ghahramani, Zoubin [1 ]
机构：
[1] NTT Communication Science Lab, Kyoto, Japan
来源：
Neural Networks for Signal Processing - Proceedings of the IEEE Workshop | 2000年 / 1卷
关键词：
Algorithms - Functions - Mathematical models - Optimization - Parameter estimation - Variational techniques;
D O I：
暂无
中图分类号：
学科分类号：
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 function 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.
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页码：145 / 154
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