A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
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
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
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
A parallel mixture of SVMs for very large scale problems
Neural Computation
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Sparse Greedy Matrix Approximation for Machine Learning
ICML '00 Proceedings of the Seventeenth 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
Efficient svm training using low-rank kernel representations
The Journal of Machine Learning Research
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
Multisurface Proximal Support Vector Machine Classification via Generalized Eigenvalues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Computation
Twin Support Vector Machines for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simpler core vector machines with enclosing balls
Proceedings of the 24th international conference on Machine learning
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
Regularized least squares fuzzy support vector regression for financial time series forecasting
Expert Systems with Applications: An International Journal
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
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
A twin-hypersphere support vector machine classifier and the fast learning algorithm
Information Sciences: an International Journal
Ensemble learning for generalised eigenvalues proximal support vector machines
International Journal of Computer Applications in Technology
Structural twin parametric-margin support vector machine for binary classification
Knowledge-Based Systems
Nonparallel hyperplane support vector machine for binary classification problems
Information Sciences: an International Journal
Least squares twin parametric-margin support vector machine for classification
Applied Intelligence
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A novel twin parametric-margin support vector machine (TPMSVM) for classification is proposed in this paper. This TPMSVM, in the spirit of the twin support vector machine (TWSVM), determines indirectly the separating hyperplane through a pair of nonparallel parametric-margin hyperplanes solved by two smaller sized support vector machine (SVM)-type problems. Similar to the parametric-margin @n@?support vector machine (par-@n@?SVM), this TPMSVM is suitable for many cases, especially when the data has heteroscedastic error structure, that is, the noise strongly depends on the input value. But there is an advantage in the learning speed compared with the par-@n@?SVM. The experimental results on several artificial and benchmark datasets indicate that the TPMSVM not only obtains fast learning speed, but also shows good generalization.