  An approach to the acceleration of parametric weak classifier boosting is
proposed. Weak classifier is called parametric if it has fixed number of
parameters and, so, can be represented as a point into multidimensional space.
Genetic algorithm is used instead of exhaustive search to learn parameters of
such classifier. Proposed approach also takes cases when effective algorithm
for learning some of the classifier parameters exists into account. Experiments
confirm that such an approach can dramatically decrease classifier training
time while keeping both training and test errors small.
