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

共 50 条

[31] Regression and classification using Gaussian process priors
Neal, RM
BAYESIAN STATISTICS 6, 1999, : 475 - 501
[32] Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation
Galy-Fajou, Theo
Wenzel, Florian
Donner, Christian
Opper, Manfred
35TH UNCERTAINTY IN ARTIFICIAL INTELLIGENCE CONFERENCE (UAI 2019), 2020, 115 : 755 - 765
[33] Efficient Optimization for Sparse Gaussian Process Regression
Cao, Yanshuai
Brubaker, Marcus A.
Fleet, David J.
Hertzmann, Aaron
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2015, 37 (12) : 2415 - 2427
[34] Chebyshev Polynomials for Efficient Gaussian Process Computation
Dudek, Adrian
Baranowski, Jerzy
2023 27TH INTERNATIONAL CONFERENCE ON METHODS AND MODELS IN AUTOMATION AND ROBOTICS, MMAR, 2023, : 240 - 245
[35] Efficient Gaussian process regression for large datasets
Banerjee, Anjishnu
Dunson, David B.
Tokdar, Surya T.
BIOMETRIKA, 2013, 100 (01) : 75 - 89
[36] Improving Gaussian Process Classification with Outlier Detection, with Applications in Image Classification
Gao, Yan
Li, Yiqun
COMPUTER VISION - ACCV 2010, PT IV, 2011, 6495 : 153 - 164
[37] An approximation of the Gaussian RBF kernel for efficient classification with SVMs
Ring, Matthias
Eskofier, Bjoern M.
PATTERN RECOGNITION LETTERS, 2016, 84 : 107 - 113
[38] First order Gaussian graphs for efficient structure classification
Bagdanov, AD
Worring, M
PATTERN RECOGNITION, 2003, 36 (06) : 1311 - 1324
[39] Scalable Gaussian Process Classification With Additive Noise for Non-Gaussian Likelihoods
Liu, Haitao
Ong, Yew-Soon
Yu, Ziwei
Cai, Jianfei
Shen, Xiaobo
IEEE TRANSACTIONS ON CYBERNETICS, 2022, 52 (07) : 5842 - 5854
[40] Integration of Gaussian Process and MRF for Hyperspectral Image Classification
Liao, Wentong
Tang, Jun
Rosenhahn, Bodo
Yang, Micheal Ying
2015 JOINT URBAN REMOTE SENSING EVENT (JURSE), 2015,

← 1 2 3 4 5 →