Support Vector Regression Machines

By Harris Drucker, C. Burges, Linda Kaufman, Alex Smola, Vladimir Naumovich Vapnik. 1996

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In Statistical Learning Theory

Citations in the corpus

Learning Parameterized Skills (2012, methodology): “We use Support Vector Regression (SVR) [20] due to their good generalization capabilities and relatively low dependence on parameter tuning.”
Sequential Model-Based Ensemble Optimization (2014, background): “In addition, we explore the -tube parameter [Drucker et al., 1997] for values ranging between 10−2 and 1 on a logarithmic scale.”
Comparison of prognostic algorithms for estimating remaining useful life of batteries (2009, methodology): “This technique was also extended to regression problems in the form of support vector regression (SVR; Drucker et al., 1997).”
A review on machine learning principles for multi-view biological data integration (2016, unknown): “Kernel matrix k ð X ; X Þ , rather than the original features, is only required as input of kernel classification (e.g. SVM [24]), regression (e.g. support vector regression [63]), clustering (e.g. spectral clustering [64]) and feature extraction (e.g. sparse representation, SR [65]) methods.”
Support Vector Machines – An Introduction (2005, methodology): “Initially developed for solving classification problems, SV techniques can be successfully applied in regression, i.e., for a functional approximation problems [ 8 , 34].”
A Tutorial on Support Vector Machines for Pattern Recognition (1998, unknown): “…estimation case, SVMs have been compared on benchmark time series prediction tests (Müller et al., 1997; Mukherjee, Osuna and Girosi, 1997), the Boston housing problem (Drucker et al., 1997), and (on artificial data) on the PET operator inversion problem (Vapnik, Golowich and Smola, 1996).”
EC-SVM approach for real-time hydrologic forecasting (2004, methodology): “SVM has been proven to be a robust and efficient algorithm for both classification (Vapnik 1995) and regression ( Drucker et al. 1997; Vapnik et al. 1997; Collobert & Bengio 2001).”
AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild (2017, background): “We also compared the proposed baselines with conventional approaches (Support Vector Machines [62] and Support Vector Regressions [63]) learned from hand-crafted features (HOG).”
On-line regression algorithms for learning mechanical models of robots: A survey (2011, background): “…svr ), the regression extension of Support Vector Machines ( svm s), it is assumed that the latent function f ( x ) can be parametrized as f ( x ) = φ ( x ) T w , where φ is a feature vector mapping the input x into some high dimensional space and w is the corresponding weight vector [31, 69, 96].”