An equivalence between sparse approximation and support vector machines
Neural Computation
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
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Metric-Based Methods for Adaptive Model Selection and Regularization
Machine Learning
A Generalized Representer Theorem
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
On different facets of regularization theory
Neural Computation
Everything old is new again: a fresh look at historical approaches in machine learning
Everything old is new again: a fresh look at historical approaches in machine learning
Learning the Kernel Function via Regularization
The Journal of Machine Learning Research
Multisurface Proximal Support Vector Machine Classification via Generalized Eigenvalues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Value Regularization and Fenchel Duality
The Journal of Machine Learning Research
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
Classifier learning with a new locality regularization method
Pattern Recognition
Discriminatively regularized least-squares classification
Pattern Recognition
Optimizing the kernel in the empirical feature space
IEEE Transactions on Neural Networks
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
| Hi-index | 0.01 |
In this paper, a novel regularization method called the local scaling heuristic-based regularization (LSHR) is proposed for binary classification. The idea in LSHR is to integrate the underlying knowledge inside the training points, including the intra-class and inter-class local information in training points. By combining the local scaling heuristic strategy, this LSHR uses two matrices defined on the intra-class and inter-class graphs of points to reflect the intra-class compactness and inter-class separability of outputs. Based on the LSHR method, two classifiers with the hinge and least squares loss functions, H-LSHR and LS-LSHR, are presented for binary classification. The experimental results on several artificial, UCI benchmark datasets and USPS digit datasets indicate the effectiveness of the proposed method.