Combining pattern recognition is a promising direction in designing effective classifiers. There are several approaches to collective decision-making, including quite popular voting methods where the decision is a combination of individual classifiers' outputs. The article focuses on the problem of fuser design which uses discriminants of individual classifiers to make a decision. We present taxonomy of proposed fusers and discuss some of their properties. We focus on the fuser which uses weights dependent on classifier and class number, because of a pretty low computational cost of its training. We formulate the problem of fuser learning as an optimization task and propose a solver which has its origin in neural computations. The quality of proposed learning algorithm was evaluated on the basis of several computer experiments, which were carried out on five benchmark datasets and their results confirm the quality of proposed concept.