DocumentCode
384286
Title
Fast on-line learning of point distribution models
Author
Al-Shaher, Abdullah A. ; Hancock, Edwin R.
Author_Institution
Dept. of Comput. Sci., York Univ., UK
Volume
2
fYear
2002
fDate
2002
Firstpage
208
Abstract
We present a fast procedure for training point distribution models (PDM) using the EM algorithm. Rather than estimating the class means and covariance matrices needed to construct the PDM, the method iteratively refines the eigenvectors of the covariance matrix using a gradient ascent technique. We evaluate the method on the problem of learning class-structure of Arabic characters.
Keywords
Gaussian distribution; character recognition; eigenvalues and eigenfunctions; iterative methods; unsupervised learning; Arabic character class-structure; EM algorithm; class means; covariance matrices; eigenvectors; fast on-line learning; gradient ascent technique; iterative refinement; learning; point distribution models; training; Computer science; Covariance matrix; Deformable models; Gaussian distribution; Iterative algorithms; Parameter estimation; Principal component analysis; Shape; Training data; Unsupervised learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 2002. Proceedings. 16th International Conference on
ISSN
1051-4651
Print_ISBN
0-7695-1695-X
Type
conf
DOI
10.1109/ICPR.2002.1048274
Filename
1048274
Link To Document