Title :
Online SVM learning: from classification to data description and back
Author :
Tax, David M J ; Laskov, Pavel
Author_Institution :
Fraunhofer FIRST.IDA, Berlin, Germany
Abstract :
The paper presents two useful extensions of the incremental SVM in the context of online learning. An online support vector data description algorithm enables application of the online paradigm to unsupervised learning. Furthermore, online learning can be used in the large-scale classification problems to limit the memory requirements for storage of the kernel matrix. The proposed algorithms are evaluated on the task of online monitoring of EEG data, and on the classification task of learning the USPS dataset with a-priori chosen working set size.
Keywords :
data analysis; data description; support vector machines; unsupervised learning; large-scale classification problems; online SVM learning; online support vector data description algorithm; unsupervised learning; Electroencephalography; Kernel; Large-scale systems; Machine learning; Machine learning algorithms; Monitoring; Supervised learning; Support vector machine classification; Support vector machines; Unsupervised learning;
Conference_Titel :
Neural Networks for Signal Processing, 2003. NNSP'03. 2003 IEEE 13th Workshop on
Print_ISBN :
0-7803-8177-7
DOI :
10.1109/NNSP.2003.1318049
