Output details
11 - Computer Science and Informatics
University of Exeter
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Article title
Rademacher Chaos Complexities for Learning the Kernel Problem
Type
D - Journal article
Title of journal
Neural Computation
Article number
-
Volume number
22
Issue number
11
First page of article
2858
ISSN of journal
1530-888X
Year of publication
2010
URL
-
Number of additional authors
1
Additional information
<24>Neural Computation is a prestigious journal in COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE with impact factor 2.387 in the year 2010 and with 5-year impact factor 2.384. This paper is a development of ideas initially published in a conference (COLT 2009) with the title "Generalization bounds for learning the kernel". That paper was widely cited as a theoretical foundation for well-known machine learning problems called learning the kernel and multiple kernel learning. This work was further taken up to develop multiple kernel learning algorithms for integrating different data sources.
Interdisciplinary
-
Cross-referral requested
-
Research group
1 - Artificial Intelligence
Citation count
3
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
