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Multi-scale kernel methods for classification
conference contribution
posted on 2005-12-01, 00:00 authored by N Kingsbury, David TayDavid Tay, M PalaniswamiWe propose the enhancement of Support Vector Machines for classification, by the use of multi-scale kernel structures (based on wavelet philosophy) which can be linearly combined in a spatially varying way. This provides a good tradeoff between ability to generalize well in areas of sparse training vectors and ability to fit fine detail of the decision surface in areas where the training vector density is sufficient to provide this information. Our algorithm is a sequential machine learning method in that progressively finer kernel functions are incorporated in successive stages of the learning process. Its key advantage is the ability to find the appropriate kernel scale for every local region of the input space. ©2005 IEEE.
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43 - 48Publisher DOI
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9780780395176ISBN-10
0780395174Publication classification
E1.1 Full written paper - refereedTitle of proceedings
2005 IEEE Workshop on Machine Learning for Signal ProcessingUsage metrics
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