Training v-Support Vector Regression: Theory and Algorithms

By Chih-Jen Lin. Neural Computation, 2002

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Incremental learning for ν-Support Vector Regression (2015, methodology): “Theorem 1 ( Chang & Lin, 2002 ) .”
LIBSVM: A library for support vector machines (2011, unknown): “Similar to ν -SVC, for regression, Sch¨olkopf et al. (2000) use a parameter ν ∈ (0 , 1] to control the number of support vectors.”
Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions (2009, unknown): “…KKT conditions for OL-SVR can be rewritten as oLD oai ¼ Xl j¼1 Qijðaj a j Þ þ e yi þ f di þ ui ¼ 0; oLD oa i ¼ Xl j¼1 Q ijðaj a j Þ þ eþ yi f d i þ u i ¼ 0; dð Þi P 0; d ð Þ i a ð Þ i ¼ 0; uð Þi P 0; u ð Þ i ða ð Þ i CÞ ¼ 0; ð3Þ where f in (3) is equal to b in (1) at optimality (Chang & Lin, 2002).”
Support Vector Regression (2007, background): “Most algorithms for SVR [19, 11, 20, 21] require that the training samples be delivered in a single batch.”
Accurate On-line Support Vector Regression (2003, methodology): “Notethat ‡ in equation 2.6 isequal to binequations2.1and2.4atoptimality ( Chang & Lin, 2002 ).”
The cross entropy method for classification (2005, methodology): “Most notably, the ν-SVM formulation ( Chang & Lin, 2002 ) attempts to control the fraction of Support Vectors (SVs) by introducing a parameter used to tradeoff the number of SVs with the size of errors.”