The nature of statistical learning theory
The nature of statistical learning theory
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
SSVM: A Smooth Support Vector Machine for Classification
Computational Optimization and Applications
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Ridge Regression Learning Algorithm in Dual Variables
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Support Vector Machine for Regression and Applications to Financial Forecasting
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
epsilon-SSVR: A Smooth Support Vector Machine for epsilon-Insensitive Regression
IEEE Transactions on Knowledge and Data Engineering
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Training a Support Vector Machine in the Primal
Neural Computation
Twin Support Vector Machines for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust support vector regression in the primal
Neural Networks
TSVR: An efficient Twin Support Vector Machine for regression
Neural Networks
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
Improvements to the SMO algorithm for SVM regression
IEEE Transactions on Neural Networks
A study on SMO-type decomposition methods for support vector machines
IEEE Transactions on Neural Networks
Fast Sparse Approximation for Least Squares Support Vector Machine
IEEE Transactions on Neural Networks
Twin Mahalanobis distance-based support vector machines for pattern recognition
Information Sciences: an International Journal
Structural twin parametric-margin support vector machine for binary classification
Knowledge-Based Systems
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Twin support vector regression (TSVR) obtains faster learning speed by solving a pair of smaller sized support vector machine (SVM)-typed problems than classical support vector regression (SVR). In this paper, a primal version for TSVR, termed primal TSVR (PTSVR), is first presented. By introducing a quadratic function to approximate its loss function, PTSVR directly optimizes the pair of quadratic programming problems (QPPs) of TSVR in the primal space based on a series of sets of linear equations. PTSVR can obviously improve the learning speed of TSVR without loss of the generalization. To improve the prediction speed, a greedy-based sparse TSVR (STSVR) in the primal space is further suggested. STSVR uses a simple back-fitting strategy to iteratively select its basis functions and update the augmented vectors. Computational results on several synthetic as well as benchmark datasets confirm the merits of PTSVR and STSVR.