Alvarez, M.A., Rosasco, L. and Lawrence, N.D. orcid.org/0000-0001-9258-1030 (2012) Kernels for Vector-Valued Functions: a Review. Foundations and Trends® in Machine Learning, 4 (3). pp. 195-266. ISSN 1935-8237
Abstract
Kernel methods are among the most popular techniques in machine learning. From a frequentist/discriminative perspective they play a central role in regularization theory as they provide a natural choice for the hypotheses space and the regularization functional through the notion of reproducing kernel Hilbert spaces. From a Bayesian/generative perspective they are the key in the context of Gaussian processes, where the kernel function is also known as the covariance function. Traditionally, kernel methods have been used in supervised learning problem with scalar outputs and indeed there has been a considerable amount of work devoted to designing and learning kernels. More recently there has been an increasing interest in methods that deal with multiple outputs, motivated partly by frameworks like multitask learning. In this paper, we review different methods to design or learn valid kernel functions for multiple outputs, paying particular attention to the connection between probabilistic and functional methods.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2012 M. A. Álvarez, L. Rosasco and N. D. Lawrence. This is an author produced version of a paper subsequently published in Foundations and Trends in Machine Learning . Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Kernel methods |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 15 Aug 2017 13:59 |
Last Modified: | 24 Mar 2018 13:06 |
Published Version: | https://doi.org/10.1561/2200000036 |
Status: | Published |
Publisher: | Now Publishers |
Refereed: | Yes |
Identification Number: | 10.1561/2200000036 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:114503 |