The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Multisurface Proximal Support Vector Machine Classification via Generalized Eigenvalues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Twin Support Vector Machines for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
TSVR: An efficient Twin Support Vector Machine for regression
Neural Networks
Feature selection in the Laplacian support vector machine
Computational Statistics & Data Analysis
Particle swarm optimization aided orthogonal forward regression for unified data modeling
IEEE Transactions on Evolutionary Computation
A new feature selection method based on support vector machines for text categorisation
International Journal of Data Analysis Techniques and Strategies
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Letters: Reduced twin support vector regression
Neurocomputing
Combined Feature Selection and Cancer Prognosis Using Support Vector Machine Regression
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Support Vector Machine Method for Multivariate Density Estimation Based on Copulas
ICICIS '11 Proceedings of the 2011 International Conference on Internet Computing and Information Services
Training twin support vector regression via linear programming
Neural Computing and Applications - Special Issue on Theory and applications of swarm intelligence
Smooth twin support vector regression
Neural Computing and Applications
A weighted twin support vector regression
Knowledge-Based Systems
Improvements to the SMO algorithm for SVM regression
IEEE Transactions on Neural Networks
Robust support vector regression networks for function approximation with outliers
IEEE Transactions on Neural Networks
Pruning error minimization in least squares support vector machines
IEEE Transactions on Neural Networks
A study on reduced support vector machines
IEEE Transactions on Neural Networks
Comments on “Pruning Error Minimization in Least Squares Support Vector Machines”
IEEE Transactions on Neural Networks
Fast Sparse Approximation for Least Squares Support Vector Machine
IEEE Transactions on Neural Networks
Improvements on Twin Support Vector Machines
IEEE Transactions on Neural Networks
A feature selection method for nonparallel plane support vector machine classification
Optimization Methods & Software
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In this paper, combining the spirit of twin hyperplanes with the fast speed of least squares support vector regression (LSSVR) yields a new regressor, termed as twin least squares support vector regression (TLSSVR). As a result, TLSSVR outperforms normal LSSVR in the generalization performance, and as opposed to other algorithms of twin hyperplanes, TLSSVR owns faster computational speed. When coping with large scale problems, this advantage is obvious. To accelerate the testing speed of TLSSVR, TLSSVR is sparsified using a simple mechanism, thus obtaining STLSSVR. In addition to introducing these algorithms above, a lot of experiments including a toy problem, several small and large scale data sets, and a gas furnace example are done. These applications demonstrate the effectiveness and efficiency of the proposed algorithms.