Kernel methods for statistical learning in computer vision and pattern recognition applications

Statistical learning-based kennel methods are rapidly replacing other empirical learning methods (e.g. neural networks) as a preferred tool for machine learning due to many attractive features: a strong basis from statistical learning theory; no computational penalty in moving from linear to non-linear models; the resulting optimization problem is convex, guaranteeing a unique global solution and consequently producing systems with excellent generalization performance. This research work introduces statistical learning for solving different problems in computer vision and pattern recognition applications.; The probability density function (pdf) estimation is a one of the major ingredients in Bayesian pattern recognition and machine learning. Many algorithms have been introduced for solving the probability density function estimation problem either in parametric or nonparametric setup. In the parametric approach, a reasonable functional form for the probability density function is assumed, as such the problem is reduced to the parameters estimation of the functional form. For estimating general density functions, the nonparametric setups are used where there is no form assumed for the density function.; 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.