We propose a manifold regularization algorithm designed to work in an on-line scenario where data arrive continuously over time and it is not feasible to completely store the data stream for training the classifier in batch mode. The On-line Laplacian One-Class SVM (OLapOCSVM) algorithm exploits both positively labeled and totally unlabeled examples, updating the classifier hypothesis as new data becomes available. The learning procedure is based on conjugate gradient descent in the primal formulation of the SVM. The on-line algorithm uses an efficient buffering technique to deal with the continuous incoming data. In particular, we define a buffering policy that is based on the current estimate of the support of the input data distribution. The experimental results on real-world data show that OLapOCSVM compares favorably with the corresponding batch algorithms, while making it possible to be applied in generic on-line scenarios with limited memory requirements.

Frandina, S., Lippi, M., Maggini, M., Melacci, S. (2013). On–Line Laplacian One–Class Support Vector Machines. In Artificial Neural Networks and Machine Learning (ICANN 2013) (pp.186-193). SPRINGER-VERLAG [10.1007/978-3-642-40728-4_24].

On–Line Laplacian One–Class Support Vector Machines

FRANDINA, SALVATORE;LIPPI, MARCO;MAGGINI, MARCO;MELACCI, STEFANO
2013-01-01

Abstract

We propose a manifold regularization algorithm designed to work in an on-line scenario where data arrive continuously over time and it is not feasible to completely store the data stream for training the classifier in batch mode. The On-line Laplacian One-Class SVM (OLapOCSVM) algorithm exploits both positively labeled and totally unlabeled examples, updating the classifier hypothesis as new data becomes available. The learning procedure is based on conjugate gradient descent in the primal formulation of the SVM. The on-line algorithm uses an efficient buffering technique to deal with the continuous incoming data. In particular, we define a buffering policy that is based on the current estimate of the support of the input data distribution. The experimental results on real-world data show that OLapOCSVM compares favorably with the corresponding batch algorithms, while making it possible to be applied in generic on-line scenarios with limited memory requirements.
978-3-642-40727-7
978-3-642-40728-4
Frandina, S., Lippi, M., Maggini, M., Melacci, S. (2013). On–Line Laplacian One–Class Support Vector Machines. In Artificial Neural Networks and Machine Learning (ICANN 2013) (pp.186-193). SPRINGER-VERLAG [10.1007/978-3-642-40728-4_24].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/47101
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