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  Bayesian Kernel Methods

Smola, A., & Schölkopf, B. (2003). Bayesian Kernel Methods. In S. Mendelson, & A. Smola (Eds.), Advanced Lectures on Machine Learning: Machine Learning Summer School 2002 Canberra, Australia, February 11–22, 2002 (pp. 65-117). Berlin, Germany: Springer. doi:10.1007/3-540-36434-X_3.

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 Creators:
Smola, AJ, Author           
Schölkopf, B1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: Bayesian methods allow for a simple and intuitive representation of the function spaces used by kernel methods. This chapter describes the basic principles of Gaussian Processes, their implementation and their connection to other kernel-based Bayesian estimation methods, such as the Relevance Vector Machine.

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 Dates: 2003-012003
 Publication Status: Issued
 Pages: -
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 Rev. Type: -
 Identifiers: BibTex Citekey: 2092
DOI: 10.1007/3-540-36434-X_3
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Title: Machine Learning Summer School 2002
Place of Event: Canberra, Australia
Start-/End Date: 2002-02-11 - 2002-02-22

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Title: Advanced Lectures on Machine Learning: Machine Learning Summer School 2002 Canberra, Australia, February 11–22, 2002
Source Genre: Proceedings
 Creator(s):
Mendelson, S, Editor
Smola, AJ1, Editor           
Affiliations:
1 External Organizations, ou_persistent22            
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 65 - 117 Identifier: ISBN: 978-3-540-00529-2

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Pages: - Volume / Issue: 2600 Sequence Number: - Start / End Page: - Identifier: -