The nature of statistical learning theory
The nature of statistical learning theory
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Support Vectors Selection by Linear Programming
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5 - Volume 5
Adaptive Sparseness for Supervised Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Exact simplification of support vector solutions
The Journal of Machine Learning Research
The Journal of Machine Learning Research
A robust minimax approach to classification
The Journal of Machine Learning Research
Use of the zero norm with linear models and kernel methods
The Journal of Machine Learning Research
A tutorial on support vector regression
Statistics and Computing
The Minimum Error Minimax Probability Machine
The Journal of Machine Learning Research
An efficient method for simplifying support vector machines
ICML '05 Proceedings of the 22nd international conference on Machine learning
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
Hi-index | 0.10 |
This paper provides a sparse learning algorithm for Support Vector Classification (SVC), called Sparse Support Vector Classification (SSVC), which leads to sparse solutions by automatically setting the irrelevant parameters exactly to zero. SSVC adopts the L"0-norm regularization term and is trained by an iteratively reweighted learning algorithm. We show that the proposed novel approach contains a hierarchical-Bayes interpretation. Moreover, this model can build up close connections with some other sparse models. More specifically, one variation of the proposed method is equivalent to the zero-norm classifier proposed in (Weston et al., 2003); it is also an extended and more flexible framework in parallel with the Sparse Probit Classifier proposed by Figueiredo (2003). Theoretical justifications and experimental evaluations on two synthetic datasets and seven benchmark datasets show that SSVC offers competitive performance to SVC but needs significantly fewer Support Vectors.