The nature of statistical learning theory
The nature of statistical learning theory
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Mlps (mono layer polynomials and multi layer perceptrons) for nonlinear modeling
The Journal of Machine Learning Research
Gene selection by sequential search wrapper approaches in microarray cancer class prediction
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Challenges for future intelligent systems in biomedicine
Bioinformatics
AUC: a statistically consistent and more discriminating measure than accuracy
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A sparse signal reconstruction perspective for source localization with sensor arrays
IEEE Transactions on Signal Processing - Part II
Support Vector Machines with L1 penalty for detecting gene-gene interactions
International Journal of Data Mining and Bioinformatics
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Design and Analysis of Classifier Learning Experiments in Bioinformatics: Survey and Case Studies
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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The development of high-throughput technology has generated a massive amount of high-dimensional data, and many of them are of discrete type. Robust and efficient learning algorithms such as LASSO [1] are required for feature selection and overfitting control. However, most feature selection algorithms are only applicable to the continuous data type. In this paper, we propose a novel method for sparse support vector machines (SVMs) with L_{p} (p