الفهرس 
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Abstract
The statistical approach to pattern recognition is among the early approaches applied in this
Field. Statistical classifiers are devided into two main categories, the parametric classifiers
and the nonparametric classifiers. The main drawback of the parametric type is that it gives
weak results on unknown data distributions. On the other hand, the nonparametric methods
require extensive amounts of design samples, storage capacity and computing power in order to give good approximations of our data distributions.
In this thesis a semiparametric model consisting of a mixture of component densities, whose
Parameters are optimized using a hybrid genetic algorithm is proposed. The optimized
Density functions using this model can be quite general, they are not directly parammetrized
Although their components are.
In our work we tried to make cooperation between a speedy local search gradient algorithm,
The Gaussian clustering algorithm and the more global perspective of a genetic algorithm.
This proposed technique was tested by applying it to a number of classification problems
Ranging from small standard data to large scale data set of fingerprinting classification and the experimental results show that the achieved improvement of classification error compared to many classifiers representing the last state of the art is significant.