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Asymmetric nu-tube ...
Asymmetric nu-tube support vector regression
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Huang, Xiaolin (author)
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Shi, Lei (author)
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- Pelckmans, Kristiaan (author)
- Uppsala universitet,Avdelningen för systemteknik,Reglerteknik
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Suykens, Johan A. K. (author)
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(creator_code:org_t)
- Elsevier BV, 2014
- 2014
- English.
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In: Computational Statistics & Data Analysis. - : Elsevier BV. - 0167-9473 .- 1872-7352. ; 77, s. 371-382
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- Finding a tube of small width that covers a certain percentage of the training data samples is a robust way to estimate a location: the values of the data samples falling outside the tube have no direct influence on the estimate. The well-known nu-tube Support Vector Regression (nu-SVR) is an effective method for implementing this idea in the context of covariates. However, the nu-SVR considers only one possible location of this tube: it imposes that the amount of data samples above and below the tube are equal. The method is generalized such that those outliers can be divided asymmetrically over both regions. This extension gives an effective way to deal with skewed noise in regression problems. Numerical experiments illustrate the computational efficacy of this extension to the nu-SVR.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
Keyword
- Robust regression
- nu-tube support vector regression
- Asymmetric loss
- Quantile regression
Publication and Content Type
- ref (subject category)
- art (subject category)
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