C-Mantec is a novel neural network constructive algorithm that combines competition between neurons
with a stable modified perceptron learning rule. The neuron learning is governed by the thermal
perceptron rule that ensures stability of the acquired knowledge while the architecture grows and while
the neurons compete for new incoming information. Competition makes it possible that even after new
units have been added to the network, existing neurons still can learn if the incoming information is
similar to their stored knowledge, and this constitutes a major difference with existing constructing
algorithms. The new algorithm is tested on two different sets of benchmark problems: a Boolean function
set used in logic circuit design and a well studied set of real world problems. Both sets were used to analyze
the size of the constructed architectures and the generalization ability obtained and to compare the results
with those from other standard and well known classification algorithms. The problem of overfitting is
also analyzed, and a new built-in method to avoid its effects is devised and successfully applied within an
active learning paradigm that filter noisy examples. The results show that the new algorithm generates
very compact neural architectures with state-of-the-art generalization capabilities.
