The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
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
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
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
epsilon-SSVR: A Smooth Support Vector Machine for epsilon-Insensitive Regression
IEEE Transactions on Knowledge and Data Engineering
Multisurface Proximal Support Vector Machine Classification via Generalized Eigenvalues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Twin Support Vector Machines for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Joint time-frequency-space classification of EEG in a brain-computer interface application
EURASIP Journal on Applied Signal Processing
Application of smoothing technique on twin support vector machines
Pattern Recognition Letters
Nonparallel plane proximal classifier
Signal Processing
Least squares twin support vector machines for pattern classification
Expert Systems with Applications: An International Journal
Improvements to the SMO algorithm for SVM regression
IEEE Transactions on Neural Networks
An improved conjugate gradient scheme to the solution of least squares SVM
IEEE Transactions on Neural Networks
Fast Sparse Approximation for Least Squares Support Vector Machine
IEEE Transactions on Neural Networks
A ν-twin support vector machine (ν-TSVM) classifier and its geometric algorithms
Information Sciences: an International Journal
Letters: Reduced twin support vector regression
Neurocomputing
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Twin Mahalanobis distance-based support vector machines for pattern recognition
Information Sciences: an International Journal
A weighted twin support vector regression
Knowledge-Based Systems
A twin-hypersphere support vector machine classifier and the fast learning algorithm
Information Sciences: an International Journal
Efficient Implementation of Nonparallel Hyperplanes Classifier
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Structural twin parametric-margin support vector machine for binary classification
Knowledge-Based Systems
Twin least squares support vector regression
Neurocomputing
Forecasting method of stock price based on polynomial smooth twin support vector regression
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
Large-scale linear nonparallel support vector machine solver
Neural Networks
Smooth Newton method for implicit Lagrangian twin support vector regression
International Journal of Knowledge-based and Intelligent Engineering Systems
Extending twin support vector machine classifier for multi-category classification problems
Intelligent Data Analysis
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The learning speed of classical Support Vector Regression (SVR) is low, since it is constructed based on the minimization of a convex quadratic function subject to the pair groups of linear inequality constraints for all training samples. In this paper we propose Twin Support Vector Regression (TSVR), a novel regressor that determines a pair of @e-insensitive up- and down-bound functions by solving two related SVM-type problems, each of which is smaller than that in a classical SVR. The TSVR formulation is in the spirit of Twin Support Vector Machine (TSVM) via two nonparallel planes. The experimental results on several artificial and benchmark datasets indicate that the proposed TSVR is not only fast, but also shows good generalization performance.