Download or read book Kernel Methods for Statistical Learning in Computer Vision and Pattern Recognition Applications written by Refaat Mokhtar Mohamed and published by . This book was released on 2005 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: The curse of dimensionality is a major difficulty which exists in the density function estimation with high dimensional data spaces. An active area of research in the pattern analysis community is to develop algorithms which cope with the dimensionality problem. The purpose of this dissertation is to present a kernel-based method for solving the density estimation problem as one of the fundamental problems in machine learning. The proposed method does not pay much attention to the dimensionality problem. The contribution of this dissertation has three folds: creating a reliable and efficient learning-based density estimation algorithm which is minimally dependent on the input space dimensionality, investigating efficient learning algorithms for the proposed approach, and investigating the performance of the proposed algorithm in different computer vision and pattern recognition applications.