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
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Normalization in Support Vector Machines
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
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
Twin Support Vector Machines for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structured large margin machines: sensitive to data distributions
Machine Learning
Generalization Error Bounds in Semi-supervised Classification Under the Cluster Assumption
The Journal of Machine Learning Research
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
A ν-twin support vector machine (ν-TSVM) classifier and its geometric algorithms
Information Sciences: an International Journal
Twin Mahalanobis distance-based support vector machines for pattern recognition
Information Sciences: an International Journal
Probabilistic outputs for twin support vector machines
Knowledge-Based Systems
Maxi–Min Margin Machine: Learning Large Margin Classifiers Locally and Globally
IEEE Transactions on Neural Networks
Structural Regularized Support Vector Machine: A Framework for Structural Large Margin Classifier
IEEE Transactions on Neural Networks
A twin-hypersphere support vector machine classifier and the fast learning algorithm
Information Sciences: an International Journal
A regularization for the projection twin support vector machine
Knowledge-Based Systems
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Twin parametric-margin support vector machine (TPMSVM) determines the more flexible parametric-margin hyperplanes through a pair of quadratic programming problems (QPPs) compared with classical support vector machine (SVM). However, it ignores the prior structural information in data. In this paper, we present a structural twin parametric-margin support vector machine (STPMSVM) for classification. The two optimization problems of STPMSVM focus on the structural information of the corresponding classes based on the cluster granularity, which is vital for designing a good classifier in different real-world problems. Furthermore, two Mahalanobis distances are respectively introduced into its corresponding QPPs based on the structural information. STPMSVM has a special case of TPMSVM when each ellipsoid cluster is a unit ball in a reproducing kernel Hilbert space. Experimental results demonstrate that STPMSVM is often superior in generalization performance to other learning algorithms.