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
A Feature Selection Newton Method for Support Vector Machine Classification
Computational Optimization and Applications
Multisurface Proximal Support Vector Machine Classification via Generalized Eigenvalues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exact 1-Norm Support Vector Machines Via Unconstrained Convex Differentiable Minimization
The Journal of Machine Learning Research
Twin Support Vector Machines for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparallel plane proximal classifier
Signal Processing
Least squares twin support vector machines for pattern classification
Expert Systems with Applications: An International Journal
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During the last few years, nonparallel plane classifiers, such as Multisurface Proximal Support Vector Machine via Generalized Eigenvalues (GEPSVM), and Least Squares TWSVM (LSTSVM), have attracted much attention. However, there are not any modifications of them that have been presented to automatically select the input features. This motivates the rush towards new classifiers. In this paper, we develop a new nonparallel plane classifier, which is designed for automatically selecting the relevant features. We first introduce a Tikhonov regularization (TR) term that is usually used for regularizing least squares into the LSTSVM learning framework, and then convert this formulation to a linear programming (LP) problem. By minimizing an exterior penalty (EP) problem of the dual of the LP formulation and using a fast generalized Newton algorithm, our method yields very sparse solutions, such that it generates a classifier that depends on only a smaller number of input features. In other words, this approach is capable of suppressing input features. This makes the classifier easier to store and faster to compute in the classification phase. Lastly, experiments on both toy and real problems disclose the effectiveness of our method.