Efficient approaches to Gaussian Process classification

被引：0

作者：

Csató, L ^{[1
]}

Fokoué, E ^{[1
]}

Opper, M ^{[1
]}

Schottky, B ^{[1
]}

Winther, O ^{[1
]}

机构：

[1] Aston Univ, Sch Engn & Appl Sci, Neural Comp Res Grp, Birmingham B4 7ET, W Midlands, England

来源：

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 12 | 2000年 / 12卷

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 three simple approximations for the calculation of the posterior mean in Gaussian Process classification. The first two methods are related to mean field ideas known in Statistical Physics. The third approach is based on Bayesian online approach which was motivated by recent results in the Statistical Mechanics of Neural Networks. We present simulation results showing: 1. that the mean field Bayesian evidence may be used for hyperparameter tuning and 2. that the online approach may achieve a low training error fast.

引用

页码：251 / 257

页数：7

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