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Gaussian Processes for Machine Learning

Gaussian Processes for Machine Learning Book Reviews 429 Hogg, R. V., McKean, J. W., and Craig, A. T. (2005), Introduction to Mathe- The book’s nine chapters cover topics ranging from supervised-learning matical Statistics (6th ed.), Upper Saddle River, NJ: Prentice-Hall. problems in regression and classification to the use of GP as a prior for Bayesian Lohr, S. L. (1999), Sampling: Design and Analysis,Pacific Grove,CA: inference. Chapter 1 provides a quick introduction to traditional methods of su- Duxbury Press. pervised learning problems and describes the need for an alternative approach Mittelhammer, R. C. (1996), Mathematical Statistics for Economics and Busi- such as GP. In Chapter 2 the authors formally define a GP, beginning with the ness Administration, New York: Springer. more familiar weight-space view and continuing to the function-space view. Särndal, C.-E., Swensson, B., and Wretman, J. (1992), Model-Assisted Survey They cover the process of combining loss functions with predictive distribu- Sampling, New York: Springer. tions to make point predictions in an optimal fashion. After a short experimen- tal example of GP models applied to a robotics task, they provide a nice tutorial on related work and the history of GPs. Elements of Information Theory (2nd ed.). Chapter 3 begins with an introduction to the http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the American Statistical Association Taylor & Francis

Gaussian Processes for Machine Learning

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References (3)

Publisher
Taylor & Francis
Copyright
© American Statistical Association
ISSN
1537-274X
eISSN
0162-1459
DOI
10.1198/jasa.2008.s219
Publisher site
See Article on Publisher Site

Abstract

Book Reviews 429 Hogg, R. V., McKean, J. W., and Craig, A. T. (2005), Introduction to Mathe- The book’s nine chapters cover topics ranging from supervised-learning matical Statistics (6th ed.), Upper Saddle River, NJ: Prentice-Hall. problems in regression and classification to the use of GP as a prior for Bayesian Lohr, S. L. (1999), Sampling: Design and Analysis,Pacific Grove,CA: inference. Chapter 1 provides a quick introduction to traditional methods of su- Duxbury Press. pervised learning problems and describes the need for an alternative approach Mittelhammer, R. C. (1996), Mathematical Statistics for Economics and Busi- such as GP. In Chapter 2 the authors formally define a GP, beginning with the ness Administration, New York: Springer. more familiar weight-space view and continuing to the function-space view. Särndal, C.-E., Swensson, B., and Wretman, J. (1992), Model-Assisted Survey They cover the process of combining loss functions with predictive distribu- Sampling, New York: Springer. tions to make point predictions in an optimal fashion. After a short experimen- tal example of GP models applied to a robotics task, they provide a nice tutorial on related work and the history of GPs. Elements of Information Theory (2nd ed.). Chapter 3 begins with an introduction to the

Journal

Journal of the American Statistical AssociationTaylor & Francis

Published: Mar 1, 2008

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