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231225s2020 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2019.2892371
|2 doi
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|a eng
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| 100 |
1 |
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|a Liu, Yong
|e verfasserin
|4 aut
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| 245 |
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|a Fast Cross-Validation for Kernel-Based Algorithms
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| 264 |
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|c 2020
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 06.04.2020
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Cross-validation (CV) is a widely adopted approach for selecting the optimal model. However, the computation of empirical cross-validation error (CVE) has high complexity due to multiple times of learner training. In this paper, we develop a novel approximation theory of CVE and present an approximate approach to CV based on the Bouligand influence function (BIF) for kernel-based algorithms. We first represent the BIF and higher order BIFs in Taylor expansions, and approximate CV via the Taylor expansions. We then derive an upper bound of the discrepancy between the original and approximate CV. Furthermore, we provide a novel computing method to calculate the BIF for general distribution, and evaluate BIF criterion for sample distribution to approximate CV. The proposed approximate CV requires training on the full data set only once and is suitable for a wide variety of kernel-based algorithms. Experimental results demonstrate that the proposed approximate CV is sound and effective
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Liao, Shizhong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Jiang, Shali
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ding, Lizhong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Lin, Hailun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Weiping
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 42(2020), 5 vom: 14. Mai, Seite 1083-1096
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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|g volume:42
|g year:2020
|g number:5
|g day:14
|g month:05
|g pages:1083-1096
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|u http://dx.doi.org/10.1109/TPAMI.2019.2892371
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