DocumentCode
3239700
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
fYear
2003
fDate
17-19 Sept. 2003
Firstpage
499
Lastpage
508
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks for Signal Processing, 2003. NNSP'03. 2003 IEEE 13th Workshop on
ISSN
1089-3555
Print_ISBN
0-7803-8177-7
Type
conf
DOI
10.1109/NNSP.2003.1318049
Filename
1318049
Link To Document