Oleg Kovárík

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Optimization of neural network topology, weights and neuron transfer functions for given data set and problem is not an easy task. In this article, we focus primarily on building optimal feed-forward neural network classifier for i.i.d. data sets. We apply meta-learning principles to the neural network structure and function optimization. We show that(More)
When parameters of model are being adjusted, model is learning to mimic the behaviour of a real world system. Optimization methods are responsible for parameters adjustment. The problem is that each real world system is different and its model should be of different complexity. It is almost impossible to decide which optimization method will perform the(More)
Meta-learning is a method of improving results of algorithm by learning from meta-features which describe problem instances and from results produced by various algorithms on these instances. In this project we tried to apply this idea, which was already proved to be useful in machine learning, to combinatorial optimization. We have developed a general(More)
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