A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
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)
A robust minimax approach to classification
The Journal of Machine Learning Research
The Minimum Error Minimax Probability Machine
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
Learning a Mahalanobis distance metric for data clustering and classification
Pattern Recognition
Least squares twin support vector machines for pattern classification
Expert Systems with Applications: An International Journal
Beyond distance measurement: constructing neighborhood similarity for video annotation
IEEE Transactions on Multimedia - Special section on communities and media computing
TSVR: An efficient Twin Support Vector Machine for regression
Neural Networks
On minimum class locality preserving variance support vector machine
Pattern Recognition
A ν-twin support vector machine (ν-TSVM) classifier and its geometric algorithms
Information Sciences: an International Journal
Knowledge based Least Squares Twin support vector machines
Information Sciences: an International Journal
Minimum Class Variance Support Vector Machines
IEEE Transactions on Image Processing
Nonlinear kernel-based statistical pattern analysis
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
A geometric approach to Support Vector Machine (SVM) classification
IEEE Transactions on Neural Networks
A study on SMO-type decomposition methods for support vector machines
IEEE Transactions on Neural Networks
Weighted Mahalanobis Distance Kernels for 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
Multi-appliance recognition system with hybrid SVM/GMM classifier in ubiquitous smart home
Information Sciences: an International Journal
An efficient classification approach for large-scale mobile ubiquitous computing
Information Sciences: an International Journal
Structural twin parametric-margin support vector machine for binary classification
Knowledge-Based Systems
Reliability assessment and failure analysis of lithium iron phosphate batteries
Information Sciences: an International Journal
Nonparallel hyperplane support vector machine for binary classification problems
Information Sciences: an International Journal
Traffic sign recognition using group sparse coding
Information Sciences: an International Journal
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Twin support vector machines (TSVMs) achieve fast training speed and good performance for data classification. However, TSVMs do not take full advantage of the statistical information in data, such as the covariance of each class of data. This paper proposes a new twin Mahalanobis distance-based support vector machine (TMSVM) classifier, in which two Mahalanobis distance-based kernels are constructed according to the covariance matrices of two classes of data for optimizing the nonparallel hyperplanes. TMSVMs have a special case of TSVMs when the covariance matrices in a reproducing kernel Hilbert space are degenerated to the identity ones. TMSVMs are suitable for many real problems, especially for the case that the covariance matrices of two classes of data are obviously different. The experimental results on several artificial and benchmark datasets indicate that TMSVMs not only possess fast learning speed, but also obtain better generalization than TSVMs and other methods.