IEICE TRANSACTIONS on Information
Recent Advances and Trends in Large-Scale Kernel Methods
Summary :
Kernel methods such as the support vector machine are one of the most successful algorithms in modern machine learning. Their advantage is that linear algorithms are extended to non-linear scenarios in a straightforward way by the use of the kernel trick. However, naive use of kernel methods is computationally expensive since the computational complexity typically scales cubically with respect to the number of training samples. In this article, we review recent advances in the kernel methods, with emphasis on scalability for massive problems.
- Publication
- IEICE TRANSACTIONS on Information Vol.E92-D No.7 pp.1338-1353
- Publication Date
- 2009/07/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E92.D.1338
- Type of Manuscript
- Special Section INVITED PAPER (Special Section on Large Scale Algorithms for Learning and Optimization)
- Category
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Hisashi KASHIMA, Tsuyoshi IDE, Tsuyoshi KATO, Masashi SUGIYAMA, "Recent Advances and Trends in Large-Scale Kernel Methods" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 7, pp. 1338-1353, July 2009, doi: 10.1587/transinf.E92.D.1338.
Abstract: Kernel methods such as the support vector machine are one of the most successful algorithms in modern machine learning. Their advantage is that linear algorithms are extended to non-linear scenarios in a straightforward way by the use of the kernel trick. However, naive use of kernel methods is computationally expensive since the computational complexity typically scales cubically with respect to the number of training samples. In this article, we review recent advances in the kernel methods, with emphasis on scalability for massive problems.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.1338/_p
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@ARTICLE{e92-d_7_1338,
author={Hisashi KASHIMA, Tsuyoshi IDE, Tsuyoshi KATO, Masashi SUGIYAMA, },
journal={IEICE TRANSACTIONS on Information},
title={Recent Advances and Trends in Large-Scale Kernel Methods},
year={2009},
volume={E92-D},
number={7},
pages={1338-1353},
abstract={Kernel methods such as the support vector machine are one of the most successful algorithms in modern machine learning. Their advantage is that linear algorithms are extended to non-linear scenarios in a straightforward way by the use of the kernel trick. However, naive use of kernel methods is computationally expensive since the computational complexity typically scales cubically with respect to the number of training samples. In this article, we review recent advances in the kernel methods, with emphasis on scalability for massive problems.},
keywords={},
doi={10.1587/transinf.E92.D.1338},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - Recent Advances and Trends in Large-Scale Kernel Methods
T2 - IEICE TRANSACTIONS on Information
SP - 1338
EP - 1353
AU - Hisashi KASHIMA
AU - Tsuyoshi IDE
AU - Tsuyoshi KATO
AU - Masashi SUGIYAMA
PY - 2009
DO - 10.1587/transinf.E92.D.1338
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2009
AB - Kernel methods such as the support vector machine are one of the most successful algorithms in modern machine learning. Their advantage is that linear algorithms are extended to non-linear scenarios in a straightforward way by the use of the kernel trick. However, naive use of kernel methods is computationally expensive since the computational complexity typically scales cubically with respect to the number of training samples. In this article, we review recent advances in the kernel methods, with emphasis on scalability for massive problems.
ER -
English
