The Support Vector Machine (SVM) is one of the most effective and used algorithms, when targeting classification. Despite its large success, SVM is mainly afflicted by two issues: (i) some hyperparameters must be tuned in advance and are, in practice, identified through computationally intensive procedures; (ii) possible a-priori knowledge about the problem (e.g. doctor expertise in medical applications) cannot be straightforwardly exploited. In this paper, we introduce a new approach, able to cope with the two previous problems: several experiments, performed on real-world benchmarking datasets, show that our method outperforms, on average, other techniques proposed in the literature.
Shrinkage learning to improve SVM with hints
ONETO, LUCA;GHIO, ALESSANDRO;RIDELLA, SANDRO;ANGUITA, DAVIDE
2015-01-01
Abstract
The Support Vector Machine (SVM) is one of the most effective and used algorithms, when targeting classification. Despite its large success, SVM is mainly afflicted by two issues: (i) some hyperparameters must be tuned in advance and are, in practice, identified through computationally intensive procedures; (ii) possible a-priori knowledge about the problem (e.g. doctor expertise in medical applications) cannot be straightforwardly exploited. In this paper, we introduce a new approach, able to cope with the two previous problems: several experiments, performed on real-world benchmarking datasets, show that our method outperforms, on average, other techniques proposed in the literature.File | Dimensione | Formato | |
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