Neural Network and Convolutional Neural Network algorithms are among the best performing machine learning algorithms. However, the performance of the algorithms may vary between multiple runs because of the stochastic nature of these algorithms. This stochastic behavior can result in weaker accuracy for a single run, and in many cases, it is hard to tell whether we should repeat the learning giving a chance to have a better result. Among the useful techniques to solve this problem, we can use the committee machine and the ensemble methods, which in many cases give better than average or even better than the best individual result. We defined new voting function variants for ensemble learner committee machine algorithms which can be used as competitors of the well-known voting functions. Some belong to the locally weighted average voting functions, others are meta voting functions calculated from the output of the previous voting functions functions called with the results of the individual learners. The performance evaluation of these methods was done from numerous learning sessions.

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