The classical SVM approach to solve multilabel problems consists in training a single classifier for each class. We propose a compact model that considers the whole set of classifiers at once. Our strategy focuses on the shared use of the kernel matrix information between different classifiers in order to reduce the complexity of the learning task. Experiments with the Reuters-21578 corpus show a speedup in term of kernel computations (cache misses) preserving state-of-the-art performance.
Speeding up the solution of multilabel problems with Support Vector Machines
AIOLLI, FABIO;SPERDUTI, ALESSANDRO
2003
Abstract
The classical SVM approach to solve multilabel problems consists in training a single classifier for each class. We propose a compact model that considers the whole set of classifiers at once. Our strategy focuses on the shared use of the kernel matrix information between different classifiers in order to reduce the complexity of the learning task. Experiments with the Reuters-21578 corpus show a speedup in term of kernel computations (cache misses) preserving state-of-the-art performance.File in questo prodotto:
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