The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
The symmetric eigenvalue problem
The symmetric eigenvalue problem
Making large-scale support vector machine learning practical
Advances in kernel methods
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
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
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)
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Convex Optimization
Multicategory Proximal Support Vector Machine Classifiers
Machine Learning
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
Application of smoothing technique on twin support vector machines
Pattern Recognition Letters
Discriminatively regularized least-squares classification
Pattern Recognition
Nonparallel plane proximal classifier
Signal Processing
Least squares twin support vector machines for pattern classification
Expert Systems with Applications: An International Journal
Novel multiclass classifiers based on the minimization of the within-class variance
IEEE Transactions on Neural Networks
On minimum class locality preserving variance support vector machine
Pattern Recognition
Multi-weight vector projection support vector machines
Pattern Recognition Letters
Face recognition using recursive Fisher linear discriminant
IEEE Transactions on Image Processing
Efficient and robust feature extraction by maximum margin criterion
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
Structural twin parametric-margin support vector machine for binary classification
Knowledge-Based Systems
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In this paper, a novel binary classifier coined projection twin support vector machine (PTSVM) is proposed. The idea is to seek two projection directions, one for each class, such that the projected samples of one class are well separated from those of the other class in its respective subspace. In order to further boost performance, a recursive algorithm for PTSVM is proposed to generate more than one projection axis for each class. To overcome the singularity problem, principal component analysis (PCA) is utilized to transform the data in the original space into a low-dimensional subspace wherein the optimization problem of PTSVM is convex and can be solved efficiently. The experimental results on several UCI benchmark data sets and USPS digit database show the feasibility and effectiveness of the proposed method.