A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning with the Set Covering Machine
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Learning with Decision Lists of Data-Dependent Features
The Journal of Machine Learning Research
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In this paper we eliminate the need for parameter estimation associated with the set covering machine (SCM) by directly minimizing generalization error bounds. Firstly, we consider a sub-optimal greedy heuristic algorithm termed the bound set covering machine (BSCM). Next, we propose the branch and bound set covering machine (BBSCM) and prove that it finds a classifier producing the smallest generalization error bound. We further justify empirically the BBSCM algorithm with a heuristic relaxation, called BBSCM(茂戮驴), which guarantees a solution whose bound is within a factor 茂戮驴of the optimal. Experiments comparing against the support vector machine (SVM) and SCM algorithms demonstrate that the approaches proposed can lead to some or all of the following: 1) faster running times, 2) sparser classifiers and 3) competitive generalization error, all while avoiding the need for parameter estimation.