Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
The nature of statistical learning theory
The nature of statistical learning theory
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Radius margin bounds for support vector machines with the RBF kernel
Neural Computation
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Learning by Kernel Polarization
Neural Computation
Gradient-Based Adaptation of General Gaussian Kernels
Neural Computation
Local Fisher discriminant analysis for supervised dimensionality reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Evolutionary tuning of multiple SVM parameters
Neurocomputing
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Hi-index | 0.01 |
The problem of evaluating the quality of a kernel function for a classification task is considered. Drawn from physics, kernel polarization was introduced as an effective measure for selecting kernel parameters, which was previously done mostly by exhaustive search. However, it only takes between-class separability into account but neglects the preservation of within-class local structure. The 'globality' of the kernel polarization may leave less degree of freedom for increasing separability. In this paper, we propose a new quality measure called local kernel polarization, which is a localized variant of kernel polarization. Local kernel polarization can preserve the local structure of the data of the same class so the data can be embedded more appropriately. This quality measure is demonstrated with some UCI machine learning benchmark examples.