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

[21] LEARNING FILTERS IN GAUSSIAN PROCESS CLASSIFICATION PROBLEMS
Ruiz, Pablo
Mateos, Javier
Molina, Rafael
Katsaggelos, Aggelos K.
2014 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2014, : 2913 - 2917
[22] Gaussian Process Classification Using Posterior Linearization
Garcia-Fernandez, Angel F.
Tronarp, Filip
Sarkka, Simo
IEEE SIGNAL PROCESSING LETTERS, 2019, 26 (05) : 735 - 739
[23] Gaussian process classification using image deformation
Williams, David P.
2007 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL II, PTS 1-3, 2007, : 605 - 608
[24] Adversarial vulnerability bounds for Gaussian process classification
Michael Thomas Smith
Kathrin Grosse
Michael Backes
Mauricio A. Álvarez
Machine Learning, 2023, 112 : 971 - 1009
[25] Bayesian Multitask Classification with Gaussian Process Priors
Skolidis, Grigorios
Sanguinetti, Guido
IEEE TRANSACTIONS ON NEURAL NETWORKS, 2011, 22 (12): : 2011 - 2021
[26] Scalable Large Margin Gaussian Process Classification
Wistuba, Martin
Rawat, Ambrish
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2019, PT II, 2020, 11907 : 501 - 516
[27] Gaussian process classification for variable fidelity data
Klyuchnikov, Nikita
Burnaev, Evgeny
NEUROCOMPUTING, 2020, 397 (397) : 345 - 355
[28] Estimation of region of attraction with Gaussian process classification
Wang, Ke
Menon, Prathyush P.
Veenman, Joost
Bennani, Samir
EUROPEAN JOURNAL OF CONTROL, 2023, 74
[29] Adversarial vulnerability bounds for Gaussian process classification
Smith, Michael Thomas
Grosse, Kathrin
Backes, Michael
Alvarez, Mauricio A.
MACHINE LEARNING, 2023, 112 (03) : 971 - 1009
[30] Variational Gaussian process for multisensor classification problems
Rohani, Neda
Ruiz, Pablo
Molina, Rafael
Katsaggelos, Aggelos K.
PATTERN RECOGNITION LETTERS, 2018, 116 : 80 - 87

← 1 2 3 4 5 →