The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
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)
Multisurface Proximal Support Vector Machine Classification via Generalized Eigenvalues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
Twin Support Vector Machines for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
MultiK-MHKS: A Novel Multiple Kernel Learning Algorithm
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
Proximal support vector machine using local information
Neurocomputing
TSVR: An efficient Twin Support Vector Machine for regression
Neural Networks
A ν-twin support vector machine (ν-TSVM) classifier and its geometric algorithms
Information Sciences: an International Journal
Localized twin SVM via convex minimization
Neurocomputing
Letters: Reduced twin support vector regression
Neurocomputing
Optimizing the kernel in the empirical feature space
IEEE Transactions on Neural Networks
Improvements on Twin Support Vector Machines
IEEE Transactions on Neural Networks
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In this paper we present a classifier called bi-density twin support vector machines (BDTWSVMs) for data classification. In the training stage, BDTWSVMs first compute the relative density degrees for all training points using the intra-class graph whose weights are determined by a local scaling heuristic strategy, then optimize a pair of nonparallel hyperplanes through two smaller sized support vector machine (SVM)-typed problems. In the prediction stage, BDTWSVMs assign to the class label depending on the kernel density degree-based distances from each test point to the two hyperplanes. BDTWSVMs not only inherit good properties from twin support vector machines (TWSVMs) but also give good description for data points. The experimental results on toy as well as publicly available datasets indicate that BDTWSVMs compare favorably with classical SVMs and TWSVMs in terms of generalization.