Learning Gradients with Gaussian Processes

Xinwei Jiang; Junbin Gao; Tianjiang Wang; Paul H Kwan

doi:10.1007/978-3-642-13672-6_12

Learning Gradients with Gaussian Processes

Xinwei Jiang, Junbin Gao, Tianjiang Wang, Paul H Kwan

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Research

1 Citation (Scopus)

Abstract

The problems of variable selection and inference of statistical dependence have been addressed by modeling in the gradients learning framework based on the representer theorem. In this paper, we propose a new gradients learning algorithm in the Bayesian framework, called Gaussian Processes Gradient Learning (GPGL) model, which can achieve higher accuracy while returning the credible intervals of the estimated gradients that existing methods cannot provide. The simulation examples are used to verify the proposed algorithm, and its advantages can be seen from the experimental results.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining: Proceedings of the 14th Pacific-Asia Conference, PAKDD 2010
Editors	Mohammed J Zaki, Jeffrey Xu Yu, B Ravindran, Vikram Pudi
Place of Publication	Berlin, Germany
Publisher	Springer
Pages	113-124
Volume	II
Edition	1
ISBN (Print)	3642136710, 9783642136719
DOIs	https://doi.org/10.1007/978-3-642-13672-6_12
Publication status	Published - 2010

Publication series

Name	Lecture Notes in Artificial Intelligence
Number	6119
ISSN (Electronic)	0302-9743

Keywords

Analysis of Algorithms and Complexity
Numerical Computation
Pattern Recognition and Data Mining

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10.1007/978-3-642-13672-6_12

https://hdl.handle.net/1959.11/7612

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Jiang, X., Gao, J., Wang, T., & Kwan, P. H. (2010). Learning Gradients with Gaussian Processes. In M. J Zaki, J. Xu Yu, B. Ravindran, & V. Pudi (Eds.), Advances in Knowledge Discovery and Data Mining: Proceedings of the 14th Pacific-Asia Conference, PAKDD 2010 (1 ed., Vol. II, pp. 113-124). (Lecture Notes in Artificial Intelligence; No. 6119). Springer. https://doi.org/10.1007/978-3-642-13672-6_12

Jiang, Xinwei ; Gao, Junbin ; Wang, Tianjiang et al. / Learning Gradients with Gaussian Processes. Advances in Knowledge Discovery and Data Mining: Proceedings of the 14th Pacific-Asia Conference, PAKDD 2010. editor / Mohammed J Zaki ; Jeffrey Xu Yu ; B Ravindran ; Vikram Pudi. Vol. II 1. ed. Berlin, Germany : Springer, 2010. pp. 113-124 (Lecture Notes in Artificial Intelligence; 6119).

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title = "Learning Gradients with Gaussian Processes",

abstract = "The problems of variable selection and inference of statistical dependence have been addressed by modeling in the gradients learning framework based on the representer theorem. In this paper, we propose a new gradients learning algorithm in the Bayesian framework, called Gaussian Processes Gradient Learning (GPGL) model, which can achieve higher accuracy while returning the credible intervals of the estimated gradients that existing methods cannot provide. The simulation examples are used to verify the proposed algorithm, and its advantages can be seen from the experimental results.",

keywords = "Analysis of Algorithms and Complexity, Numerical Computation, Pattern Recognition and Data Mining",

author = "Xinwei Jiang and Junbin Gao and Tianjiang Wang and Kwan, \{Paul H\}",

year = "2010",

doi = "10.1007/978-3-642-13672-6\_12",

language = "English",

isbn = "3642136710",

volume = "II",

series = "Lecture Notes in Artificial Intelligence",

publisher = "Springer",

number = "6119",

pages = "113--124",

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booktitle = "Advances in Knowledge Discovery and Data Mining: Proceedings of the 14th Pacific-Asia Conference, PAKDD 2010",

edition = "1",

}

Jiang, X, Gao, J, Wang, T & Kwan, PH 2010, Learning Gradients with Gaussian Processes. in M J Zaki, J Xu Yu, B Ravindran & V Pudi (eds), Advances in Knowledge Discovery and Data Mining: Proceedings of the 14th Pacific-Asia Conference, PAKDD 2010. 1 edn, vol. II, Lecture Notes in Artificial Intelligence, no. 6119, Springer, Berlin, Germany, pp. 113-124. https://doi.org/10.1007/978-3-642-13672-6_12

Learning Gradients with Gaussian Processes. / Jiang, Xinwei; Gao, Junbin; Wang, Tianjiang et al.
Advances in Knowledge Discovery and Data Mining: Proceedings of the 14th Pacific-Asia Conference, PAKDD 2010. ed. / Mohammed J Zaki; Jeffrey Xu Yu; B Ravindran; Vikram Pudi. Vol. II 1. ed. Berlin, Germany: Springer, 2010. p. 113-124 (Lecture Notes in Artificial Intelligence; No. 6119).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Research

TY - CHAP

T1 - Learning Gradients with Gaussian Processes

AU - Jiang, Xinwei

AU - Gao, Junbin

AU - Wang, Tianjiang

AU - Kwan, Paul H

PY - 2010

Y1 - 2010

N2 - The problems of variable selection and inference of statistical dependence have been addressed by modeling in the gradients learning framework based on the representer theorem. In this paper, we propose a new gradients learning algorithm in the Bayesian framework, called Gaussian Processes Gradient Learning (GPGL) model, which can achieve higher accuracy while returning the credible intervals of the estimated gradients that existing methods cannot provide. The simulation examples are used to verify the proposed algorithm, and its advantages can be seen from the experimental results.

AB - The problems of variable selection and inference of statistical dependence have been addressed by modeling in the gradients learning framework based on the representer theorem. In this paper, we propose a new gradients learning algorithm in the Bayesian framework, called Gaussian Processes Gradient Learning (GPGL) model, which can achieve higher accuracy while returning the credible intervals of the estimated gradients that existing methods cannot provide. The simulation examples are used to verify the proposed algorithm, and its advantages can be seen from the experimental results.

KW - Analysis of Algorithms and Complexity

KW - Numerical Computation

KW - Pattern Recognition and Data Mining

UR - http://trove.nla.gov.au/work/38088900

UR - https://www.scopus.com/pages/publications/79956312571

U2 - 10.1007/978-3-642-13672-6_12

DO - 10.1007/978-3-642-13672-6_12

M3 - Chapter

SN - 3642136710

SN - 9783642136719

VL - II

T3 - Lecture Notes in Artificial Intelligence

SP - 113

EP - 124

BT - Advances in Knowledge Discovery and Data Mining: Proceedings of the 14th Pacific-Asia Conference, PAKDD 2010

A2 - J Zaki, Mohammed

A2 - Xu Yu, Jeffrey

A2 - Ravindran, B

A2 - Pudi, Vikram

PB - Springer

CY - Berlin, Germany

ER -

Jiang X, Gao J, Wang T, Kwan PH. Learning Gradients with Gaussian Processes. In J Zaki M, Xu Yu J, Ravindran B, Pudi V, editors, Advances in Knowledge Discovery and Data Mining: Proceedings of the 14th Pacific-Asia Conference, PAKDD 2010. 1 ed. Vol. II. Berlin, Germany: Springer. 2010. p. 113-124. (Lecture Notes in Artificial Intelligence; 6119). doi: 10.1007/978-3-642-13672-6_12

Learning Gradients with Gaussian Processes

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