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Observations Concerning Reconstructions with Local Support
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-5698-5983
2002 (English)Report (Other academic)
Abstract [en]

This report describes how the choice of kernel affects a non-parametric density estimation. Methods for accurate localisation of peaks in the estimated densities are developed for Gaussian and cos2 kernels. The accuracy and robustness of the peak localisation methods are studied with respect to noise, number of samples, and interference between peaks. Although the peak localisation is formulated in the framework of non-parametric density estimation, the results are also applicable to associative learning with localised responses.

Place, publisher, year, edition, pages
Linköping, Sweden: Linköping University, Department of Electrical Engineering , 2002. , 15 p.
LiTH-ISY-R, ISSN 1400-3902 ; 2425
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-53420ISRN: LiTH-ISY-R-2425OAI: diva2:288272
Available from: 2010-01-20 Created: 2010-01-20 Last updated: 2015-12-10Bibliographically approved

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Forssen, Per-Erik
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Computer VisionThe Institute of Technology
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