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
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Kernel projection algorithm for large-scale SVM problems
Journal of Computer Science and Technology
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Duality and Geometry in SVM Classifiers
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 Machines: Training and Applications
Support Vector Machines: Training and Applications
Support vector interval regression networks for interval regression analysis
Fuzzy Sets and Systems - Theme: Learning and modeling
A generalized S-K algorithm for learning v-SVM classifiers
Pattern Recognition Letters
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
A general soft method for learning SVM classifiers with L1-norm penalty
Pattern Recognition
Application of smoothing technique on twin support vector machines
Pattern Recognition Letters
Nonparallel plane proximal classifier
Signal Processing
Associated evolution of a support vector machine-based classifier for pedestrian detection
Information Sciences: an International Journal
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 fast iterative nearest point algorithm for support vector machine classifier design
IEEE Transactions on Neural Networks
A geometric approach to Support Vector Machine (SVM) classification
IEEE Transactions on Neural Networks
Twin Mahalanobis distance-based support vector machines for pattern recognition
Information Sciences: an International Journal
A weighted twin support vector regression
Knowledge-Based Systems
Probabilistic outputs for twin support vector machines
Knowledge-Based Systems
A twin-hypersphere support vector machine classifier and the fast learning algorithm
Information Sciences: an International Journal
Information Sciences: an International Journal
Structural twin parametric-margin support vector machine for binary classification
Knowledge-Based Systems
Recognizing architecture styles by hierarchical sparse coding of blocklets
Information Sciences: an International Journal
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In this paper, a @n-twin support vector machine (@n-TSVM) is presented, improving upon the recently proposed twin support vector machine (TSVM). This @n-TSVM introduces a pair of parameters (@n) to control the bounds of the fractions of the support vectors and the error margins. The theoretical analysis shows that this @n-TSVM can be interpreted as a pair of minimum generalized Mahalanobis-norm problems on two reduced convex hulls (RCHs). Based on the well-known Gilbert's algorithm, a geometric algorithm for TSVM (GA-TSVM) and its probabilistic speed-up version, named PGA-TSVM, are presented. Computational results on several synthetic as well as benchmark datasets demonstrate the significant advantages of the proposed algorithms in terms of both computation complexity and classification accuracy.