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Output details

11 - Computer Science and Informatics

Royal Holloway, University of London

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Article title

An identity for kernel ridge regression

Type
D - Journal article
Title of journal
Theoretical Computer Science
Article number
-
Volume number
473
Issue number
-
First page of article
157
ISSN of journal
0304-3975
Year of publication
2013
Number of additional authors
1
Additional information

<24>This paper was selected by the ALT2010 programme committee for a journal special issue. Batch and on-line are the two fundamentally different scenarios of machine learning usually requiring different learning methods or separate theoretical analysis. This paper compares the use of ridge regression (Gaussian processes regression) in these two modes and establishes a general identity connecting the loss of ridge regression in the two modes. The performance of ridge regression in both cases on compact domains is shown to match asymptotically. The paper impacts on practical applications of regression in robotics, for example at the

Italian Institute of Technology, Genoa.

Interdisciplinary
-
Cross-referral requested
-
Research group
B - Computer Learning Research Centre
Citation count
0
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-