Boosting in the limit: maximizing the margin of learned ensembles
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Parallel Optimization: Theory, Algorithms and Applications
Parallel Optimization: Theory, Algorithms and Applications
Linear Programming Boosting via Column Generation
Machine Learning
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Maximal margin classification for metric spaces
Journal of Computer and System Sciences - Special issue: Learning theory 2003
Exact 1-Norm Support Vector Machines Via Unconstrained Convex Differentiable Minimization
The Journal of Machine Learning Research
A theory of learning with similarity functions
Machine Learning
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Theory and algorithm for learning with dissimilarity functions
Neural Computation
Efficient large scale linear programming support vector machines
ECML'06 Proceedings of the 17th European conference on Machine Learning
Sparse substring pattern set discovery using linear programming boosting
DS'10 Proceedings of the 13th international conference on Discovery science
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We propose a new boosting algorithm based on a linear programming formulation. Our algorithm can take advantage of the sparsity of the solution of the underlying optimization problem. In preliminary experiments, our algorithm outperforms a state-of-the-art LP solver and LPBoost especially when the solution is given by a small set of relevant hypotheses and support vectors.