Adaboost; Boosting; MNIST data; Pattern recognition

The methods proposed in the article come out from a technique called boosting, which is based on the principle of combining a large number of so-called weak classifiers into a strong classifier. The article is focused on the possibility of increasing the efficiency of the algorithms via their appropriate combination, and particularly increasing their reliability and reducing their time exigency. Time exigency does not mean time exigency of the algorithm itself, nor its development, but time exigency of applying the algorithm to a particular problem domain. Simulations and experiments of the proposed processes were performed in the designed and created application environment. Experiments have been conducted over the MNIST database of handwritten digits that is commonly used for training and testing in the field of machine learning. Finally, a comparative experimental study with other approaches is presented. All achieved results are summarized in a conclusion.