Machine Learning
The symmetric eigenvalue problem
The symmetric eigenvalue problem
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
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
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A tutorial on support vector regression
Statistics and Computing
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
Twin Support Vector Machines for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
A support vector machine-based model for detecting top management fraud
Knowledge-Based Systems
Distance difference and linear programming nonparallel plane classifier
Expert Systems with Applications: An International Journal
An improved training algorithm for nonlinear kernel discriminants
IEEE Transactions on Signal Processing - Part I
Probabilistic outputs for twin support vector machines
Knowledge-Based Systems
Reduced Support Vector Machines: A Statistical Theory
IEEE Transactions on Neural Networks
Improvements on Twin Support Vector Machines
IEEE Transactions on Neural Networks
Probabilistic support vector machines for classification of noise affected data
Information Sciences: an International Journal
A regularization for the projection twin support vector machine
Knowledge-Based Systems
Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions
Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions
Structural twin support vector machine for classification
Knowledge-Based Systems
Nonparallel hyperplane support vector machine for binary classification problems
Information Sciences: an International Journal
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Note that for GEPSVM proposed in [1], the predicted class of a testing point is determined by comparing two distances between the testing point and two hyperplanes, while the optimization problems are based on comparing two distances between a hyperplane and two kinds of the training points. So there exists some inconformity between the decision process and the training process. In this paper, we propose a new proximal classifier, called PCC for short, with consistency, which is always based on comparing two distances between a point (the testing point in the decision process and the training point in the training process) and two hyperplanes. This consistency not only makes our PCC to be more reasonable logically, but also naturally leads to a simpler decision function with less computation cost. Furthermore, in our PCC two general eigenvalue problems in GEPSVM are replaced by two simple eigenvalues problems with a parameter @d. In addition, different regularization terms are introduced in the formulation of our PCC, avoiding the singular problems possibly appeared in GEPSVM. Experimental results on several benchmark data sets show that our PCC is not only faster, but also has better generalization.