Training <i>v</i>-Support Vector Regression: Theory and Algorithms
Neural ComputationPublished 1 August 2002
Chih-Chung Chang, Chih‐Jen Lin
Citations315
SJR quartileQ1
SJR score0.83
SNIP1.45
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
Some issues that do not occur in the case of classification are discussed, including the possible range of epsilon and the scaling of target values.
Abstract
We discuss the relation between epsilon-support vector regression (epsilon-SVR) and nu-support vector regression (nu-SVR). In particular, we focus on properties that are different from those of C-support vector classification (C-SVC) and nu-support vector classification (nu-SVC). We then discuss some issues that do not occur in the case of classification: the possible range of epsilon and the scaling of target values. A practical decomposition method for nu-SVR is implemented, and computational experiments are conducted. We show some interesting numerical observations specific to regression.
Keywords
Computer ScienceEngineering
ACM Transactions on Intelligent Systems and TechnologyLIBSVM
41,340 Citations2011Chih-Chung Chang, Chih‐Jen Lin
Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
TechnometricsStatistical Learning Theory
26,913 Citations1999Yuhai Wu, Vladimir Vapnik
Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
International Conference on Neural Information ProcessingAdvances in kernel methods: support vector learning
5,815 Citations1999Bernhard Schölkopf, Christopher J. C. Burges +1 more
Support vector machines for dynamic reconstruction of a chaotic system, Klaus-Robert Muller et al pairwise classification and support vector machines, Ulrich Kressel.
The MIT Press eBooksFast Training of Support Vector Machines Using Sequential Minimal Optimization
5,462 Citations1998John Platt
Technical reportsMaking Large-Scale SVM Learning Practical
4,317 Citations2006Thorsten Joachims
This chapter presents algorithmic and computational results developed for SVM light V 2.0, which make large-scale SVM training more practical and give guidelines for the application of SVMs to large domains.
Neural ComputationNew Support Vector Algorithms
2,816 Citations2000Bernhard Schölkopf, Alex Smola +2 more
A new class of support vector algorithms for regression and classification that eliminates one of the other free parameters of the algorithm: the accuracy parameter in the regression case, and the regularization constant C in the classification case.
Training support vector machines: an application to face detection
2,496 Citations2002E. Osuna, Robert M. Freund +1 more
A decomposition algorithm that guarantees global optimality, and can be used to train SVM's over very large data sets is presented, and the feasibility of the approach on a face detection problem that involves a data set of 50,000 data points is demonstrated.
TechnometricsMachine Learning, Neural and Statistical Classification
2,188 Citations1995Bill Fulkerson, D. Michie +2 more
IEEE Transactions on Neural NetworksImprovements to the SMO algorithm for SVM regression
861 Citations2000Shirish Shevade, S. Sathiya Keerthi +2 more
Using clues from the KKT conditions for the dual problem, two threshold parameters are employed to derive modifications of SMO for regression that perform significantly faster than the original SMO on the datasets tried.
Neural ComputationTraining <i>v</i>-Support Vector Classifiers: Theory and Algorithms
503 Citations2001Chih-Chung Chang, Chih‐Jen Lin
Neural ComputationAccurate On-line Support Vector Regression
403 Citations2003Junshui Ma, James Theiler +1 more
An accurate on-line support vector regression (AOSVR) that efficiently updates a trained SVR function whenever a sample is added to or removed from the training set.
Machine LearningEfficient SVM Regression Training with SMO
317 Citations2002Gary William Flake, Steve Lawrence
This work generalizes SMO so that it can handle regression problems, and addresses problems with several modifications that enable caching to be effectively used with SMO.
Machine LearningConvergence of a Generalized SMO Algorithm for SVM Classifier Design
306 Citations2002S. Sathiya Keerthi, Éric Gilbert
Convergence of a generalized version of the modified SMO algorithms given by Keerthi et al. for SVM classifier design is proved and the results are extended to modifiedSMO algorithms for solving ν-SVM classifiers problems.
Machine LearningA Simple Decomposition Method for Support Vector Machines
300 Citations2002Chih‐Wei Hsu, Chih‐Jen Lin
This paper through the design of decomposition methods for bound-constrained SVM formulations it is demonstrated that the working set selection is not a trivial task and a simple selection is proposed which leads to faster convergences for difficult cases.
IEEE Transactions on Neural NetworksOn the convergence of the decomposition method for support vector machines
249 Citations2001Chih‐Jen Lin
The asymptotic convergence of the algorithm used by the software SVM(light) and other later implementation is proved and the size of the working set can be any even number.
Shrinking the Tube: A New Support Vector Regression Algorithm
181 Citations1998Bernhard Schölkopf, Peter L. Bartlett +2 more
Neural Information Processing SystemsA geometric interpretation of ν-SVM classifiers
75 Citations1999David J. Crisp, Christopher J. C. Burges
It is shown that the recently proposed variant of the Support Vector machine (SVM) algorithm, known as ν-SVM, can be interpreted as a maximal separation between subsets of the convex hulls of the data, which are called soft convex Hulls.
Machine LearningFeasible Direction Decomposition Algorithms for Training Support Vector Machines
53 Citations2002Pavel Laskov
It is proved that the two algorithms are equivalent for the pattern recognition SVM, and the feasible direction interpretation of the maximal inconsistency algorithm is given for the regression SVM.
Neural ComputationAdaptive Two-Pass Median Filter Based on Support Vector Machines for Image Restoration
43 Citations2004Tzu-Chao Lin, Pao-Ta Yu
An Improved Decomposition Algorithm for Regression Support Vector Machines
27 Citations1999Pavel Laskov
A new decomposition algorithm for training regression Support Vector Machines (SVM) that builds on the basic principles of decomposition proposed by Osuna et.