An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
AI Game Programming Wisdom
Introduction to Linear Optimization
Introduction to Linear Optimization
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
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
The Journal of Machine Learning Research
Some greedy learning algorithms for sparse regression and classification with mercer kernels
The Journal of Machine Learning Research
Use of the zero norm with linear models and kernel methods
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Sparseness of support vector machines
The Journal of Machine Learning Research
Convex Optimization
The Journal of Machine Learning Research
Trading convexity for scalability
ICML '06 Proceedings of the 23rd international conference on Machine learning
A Direct Method for Building Sparse Kernel Learning Algorithms
The Journal of Machine Learning Research
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A new sparsity driven kernel classifier is presented based on the minimization of a recently derived data-dependent generalization error bound. The objective function consists of the usual hinge loss function penalizing training errors and a concave penalty function of the expansion coefficients. The problem of minimizing the non-convex bound is addressed by a successive linearization approach, whereby the problem is transformed into a sequence of linear programs. The algorithm produced comparable error rates to the standard support vector machine but significantly reduced the number of support vectors and the concomitant classification time.