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
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
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)
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
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
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
Joint time-frequency-space classification of EEG in a brain-computer interface application
EURASIP Journal on Applied Signal Processing
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
Least squares twin support vector machines for pattern classification
Expert Systems with Applications: An International Journal
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
A twin-hypersphere support vector machine classifier and the fast learning algorithm
Information Sciences: an International Journal
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The twin support vector hypersphere (TSVH) is a novel efficient pattern recognition tool, because it determines a pair of hyperspheres by solving two related SVM-type problems, each of which is smaller than in a classical SVM. In this paper we formulate a least squares version for this classifier, termed as the least squares twin support vector hypersphere (LS-TSVH). This formulation leads to extremely simple and fast algorithm for generating binary classifier based on a pair of hyperspheres. Due to equality type constraints in the formulation, the solution follows from solving two sets of nonlinear equations, instead of the two dual quadratic programming problems (QPPs) for TSVH. We show that the two sets of nonlinear equations are solved using the well-known Newton downhill algorithm. The effectiveness of proposed LS-TSVH is demonstrated by experimental results on several artificial and benchmark datasets.