Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/49345 
Erscheinungsjahr: 
2003
Schriftenreihe/Nr.: 
Technical Report No. 2003,04
Verlag: 
Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen, Dortmund
Zusammenfassung: 
The analysis of temporal data is an important issue of 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 that can be made and prior knowledge one has 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. This paper will present a concise overview of the application Support Vector Machines to different temporal learning tasks and the corresponding temporal representations.
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe
155.09 kB
166.72 kB





Publikationen in EconStor sind urheberrechtlich geschützt.