Ruping, S.
Morik, K.
Dept. of Comput. Sci., Dortmund Univ., Germany;
This paper appears in: Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on
Publication Date: 6-10 April 2003
Volume: 4,
On page(s): IV- 864-7 vol.4
ISSN: 1520-6149
ISBN: 0-7803-7663-3
INSPEC Accession Number: 7816343
Posted online: 2003-06-05 10:22:29.0
Abstract
The analysis of temporal data is an important issue in current research, because most real-world data either explicitly or implicitly contains some information about time. The key to successfully solving temporal learning tasks is to analyze the assumptions and prior knowledge that can be made about the temporal process of the learning problem and find a representation of the data and a learning algorithm that makes effective use of this knowledge. The paper presents a concise overview of the application of support vector machines to different temporal learning tasks and the corresponding temporal representations.
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