A tutorial on support vector regression

By Alex Smola, Bernhard Schölkopf. 2004

12,903 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Optimization & Training, Algorithms & Complexity

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Citations in the corpus

Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales (2005, methodology): “In particular, we consider linear, ε -insensitive SVM regression (Vapnik, 1995; Smola and Sch¨olkopf, 1998); the idea is to ﬁnd the hyperplane that best ﬁts the training data, but where training points whose labels are within distance ε of the hyperplane incur no loss.”
Time-series forecasting with deep learning: a survey (2020, background): “Indeed, a rich body of literature exists for automated approaches to time-series forecasting—including automatic parametric model selection [18], and traditional machine learning methods such as kernel regression [19] and support vector regression [20].”
Deep Learning for Forecasting Stock Returns in the Cross-Section (2018, methodology): “The problem definition for SVR is  -SVR [16] which is implemented with the class “sklearn.svm.SVR”.”
Scene Flow to Action Map: A New Representation for RGB-D Based Action Recognition with Convolutional Neural Networks (2017, methodology): “Learning d is formulated as a convex optimization problem using RankSVM [50]: d∗ = ρ(X1, ..., XT ;ϕ) = argmin d E(d), E(d) = λ 2 ‖ d ‖2 + 2 T (T − 1) × ∑ q>t max{0, 1− S(q|d) + S(t|d)}.”
A Comprehensive Survey on Transfer Learning (2019, background): “[113] also introduced and added a new term to the above objective function based on ε-insensitive loss function [119], which makes the resultant model have high computational efficiency.”
Genomic Selection in Plant Breeding: A Comparison of Models (2012, methodology): “Support vector regression (Smola and Schölkopf, 2004) uses linear models to implement nonlinear regression by mapping the input space (the marker dataset) to a feature space of a diff erent dimension (lower in the case of GS) using a nonlinear kernel function followed by linear regression in this fe”
Who Also Likes It? Generating the Most Persuasive Social Explanations in Recommender Systems (2014, background): “Details of SVR can be found in (Smola and Schölkopf 2004).”
Recent Advanced Statistical Background Modeling for Foreground Detection - A Systematic Survey (2011, background): “This type of SVR is usually referred to as ε - insensitive SVR [181].”
Crashworthiness optimization of foam-filled tapered thin-walled structure using multiple surrogate models (2013, background): “1989), support vector regression (SVR) (Smola and Scholkopf 2004), and radial basis function (RBF) (Gutmann 2001; Sun et al.”
What's in a hashtag?: content based prediction of the spread of ideas in microblogging communities (2012, methodology): “2, we also applied analytic regression algorithm and Support Vector Machine for regression (SVR) [31] with various kernels and optimization of hyper parameters (both implemented as part of the Weka toolkit [16]).”
Recent advances in surrogate-based optimization (2009, background): “(54) This is one of the Karush-Kuhn-Tucker conditions, which hold at the optimum (see, e.g. Schölkopf and Smola [65]).”
Modeling and forecasting building energy consumption: A review of data-driven techniques (2019, methodology): “For forecasting applications, the process is similar to the resolution of a regression problem and is called support vector regression (SVR) (Smola & Schölkopf, 2004).”