Object structure

Solving Support Vector Machine with Many Examples, Journal of Telecommunications and Information Technology, 2010, nr 3


Białoń, Paweł

Subject and Keywords:

data mining ; network failure detection ; concept drift ; stream processing ; support vector machines ; convex optimization


Various methods of dealing with linear support vector machine (SVM) problems with a large number of examples are presented and compared. The author believes that some interesting conclusions from this critical analysis applies to many new optimization problems and indicates in which direction the science of optimization will branch in the future. This direction is driven by the automatic collection of large data to be analyzed, and is most visible in telecommunications. A stream SVM approach is proposed, in which the data substantially exceeds the available fast random access memory (RAM) due to a large number of examples. Formally, the use of RAM is constant in the number of examples (though usually it depends on the dimensionality of the examples space). It builds an inexact polynomial model of the problem. Another author’s approach is exact. It also uses a constant amount of RAM but also auxiliary disk files, that can be long but are smartly accessed. This approach bases on the cutting plane method, similarly as Joachims’ method (which, however, relies on early finishing the optimization).


Instytut Łączności - Państwowy Instytut Badawczy, Warszawa


2010, nr 3

Resource Type:




Resource Identifier:

ISSN 1509-4553, on-line: ISSN 1899-8852


Journal of Telecommunications and Information Technology



Rights Management:

Biblioteka Naukowa Instytutu Łączności



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