Ensemble of SVMs for incremental learning
6th International Workshop on Multiple Classifier Systems, MCS 2005, Seaside, CA., Amerika Birleşik Devletleri, 13 - 15 Haziran 2005, cilt.3541 3541 LNCS, ss.246-256, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Cilt numarası: 3541 3541 LNCS
- Doi Numarası: 10.1007/11494683_25
- Basıldığı Şehir: Seaside, CA.
- Basıldığı Ülke: Amerika Birleşik Devletleri
- Sayfa Sayıları: ss.246-256
- Boğaziçi Üniversitesi Adresli: Evet
Özet
Support Vector Machines (SVMs) have been successfully applied to solve a large number of classification and regression problems. However, SVMs suffer from the catastrophic forgetting phenomenon, which results in loss of previously learned information. Learn++ have recently been introduced as an incremental learning algorithm. The strength of Learn++ lies in its ability to learn new data without forgetting previously acquired knowledge and without requiring access to any of the previously seen data, even when the new data introduce new classes. To address the catastrophic forgetting problem and to add the incremental learning capability to SVMs, we propose using an ensemble of SVMs trained with Learn++. Simulation results on real-world and benchmark datasets suggest that the proposed approach is promising. © Springer-Verlag Berlin Heidelberg 2005.