Multi-resolution Support Vector Machine

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations


The Support Vector Machine (SVM) is a new learning methodology based on Vapnik-Chervonenkis (VC) theory. SVM has recently attracted growing research interest due to its ability to learn classification and regression tasks with high-dimensional data. The SVM formulation uses kernel representation. The existing algorithm leaves the choice of the kernel type and kernel parameters to the user. This paper describes an important extension to the SVM method: the Multi-resolution SVM (M-SVM), in which several kernels of different scales can be used simultaneously to approximate the target function. The proposed M-SVM approach enables `automatic' selection of the `optimal' kernel width. This usually results in better prediction accuracy of SVM models.

Original languageEnglish (US)
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Number of pages6
StatePublished - Dec 1 1999
EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
Duration: Jul 10 1999Jul 16 1999


OtherInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA


Dive into the research topics of 'Multi-resolution Support Vector Machine'. Together they form a unique fingerprint.

Cite this