The nature of statistical learning theory
The nature of statistical learning theory
Matrix computations (3rd ed.)
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Multisurface Proximal Support Vector Machine Classification via Generalized Eigenvalues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Twin Support Vector Machines for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminatively regularized least-squares classification
Pattern Recognition
Least squares twin support vector machines for pattern classification
Expert Systems with Applications: An International Journal
Multi-weight vector projection support vector machines
Pattern Recognition Letters
Localized twin SVM via convex minimization
Neurocomputing
1-Norm least squares twin support vector machines
Neurocomputing
Face recognition using recursive Fisher linear discriminant
IEEE Transactions on Image Processing
Recursive Support Vector Machines for Dimensionality Reduction
IEEE Transactions on Neural Networks
Improvements on Twin Support Vector Machines
IEEE Transactions on Neural Networks
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During the last few years, multiple surface classification (MSC) algorithms, such as projection twin support vector machine (PTSVM), and least squares PTSVM (LSPTSVM), have attracted much attention. However, there are not any modifications of them that have been presented to handle nonlinear classification. This motivates the rush towards new classifiers. In this paper, we formulate a nonlinear version of the recently proposed LSPTSVM for binary nonlinear classification by introducing nonlinear kernel into LSPTSVM. This formulation leads to a novel nonlinear algorithm, called nonlinear LSPTSVM (NLSPTSVM). Additionally, in order to promote its generalization capability, we also extend the recursive leaning method, used for further boosting the performance of PTSVM and LSPTSVM, to the nonlinear case. Experimental results on synthetic datasets, UCI datasets and NDC datasets show that NLSPTSVM has better classification capability.