Title :
Radial basis function classification as computationally efficient kernel regression
Author :
Holmström, Lase ; Hoti, Fabian
Author_Institution :
Rolf Nevanlinna Inst., Helsinki Univ., Finland
Abstract :
We consider pattern classification using radial basis function expansions. Such expansions are viewed as computationally efficient forms of kernel regression widely used in statistical literature. The performance of the proposed algorithms are tested in two case studies using speech and handwritten digit data
Keywords :
Bayes methods; character recognition; feedforward neural nets; pattern classification; probability; speech recognition; statistical analysis; handwritten digit data; kernel regression; pattern classification; radial basis function classification; radial basis function expansions; speech data; Bayesian methods; Handwriting recognition; Kernel; Pattern recognition; Polynomials; Probability density function; Speech recognition; Statistics; Taxonomy; Testing;
Conference_Titel :
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
0-7803-4859-1
DOI :
10.1109/IJCNN.1998.685963
