The nature of statistical learning theory
The nature of statistical learning theory
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
The Entire Regularization Path for the Support Vector Machine
The Journal of Machine Learning Research
Fast SVM Training Algorithm with Decomposition on Very Large Data Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Computation
Improvements to the SMO algorithm for SVM regression
IEEE Transactions on Neural Networks
Fast Sparse Approximation for Least Squares Support Vector Machine
IEEE Transactions on Neural Networks
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This paper presents a new support vector machine for simultaneous gene selection and microarray classification. By introducing the adaptive elastic net penalty which is a convex combination of weighted 1-norm penalty and weighted 2-norm penalty, the proposed support vector machine can encourage an adaptive grouping effect and reduce the shrinkage bias for the large coefficients. According to a reasonable correlation between the two regularization parameters, the optimal coefficient paths are shown to be piecewise linear and the corresponding solving algorithm is developed. Experiments are performed on leukaemia data that verify the research results.