Title :
Shrinkage learning to improve SVM with hints
Author :
Luca Oneto;Alessandro Ghio;Sandro Ridella;Davide Anguita
Author_Institution :
DITEN Department, University of Genoa, Via Opera Pia 11A, I-16145, Italy
fDate :
7/1/2015 12:00:00 AM
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.
Keywords :
"Biomedical optical imaging","Biology"
Conference_Titel :
Neural Networks (IJCNN), 2015 International Joint Conference on
Electronic_ISBN :
2161-4407
DOI :
10.1109/IJCNN.2015.7280412