Practical pattern-classification and knowledge-discovery problems require the selection of a
subset of attributes or features to represent the patterns to be classified. The authors' …

In conventional neural networks, we have to define the architecture prior to training but in
constructive neural networks the network architecture is constructed during the training …

In recent decades, Brazil has undergone several transformations, from a closed economy to
a market economy. Transport, processing and distribution of orders remained follow these …

In this paper, a proposed algorithm that dynamically changes the neural network structure is
presented. The structure is changed based on some features in the cascade correlation …

Multi-layer networks of threshold logic units (TLU) offer an attractive framework for the
design of pattern classification systems. A new constructive neural network learning …

The application of neural networks in the data mining has become wider. Although neural
networks may have complex structure, long training time, and the representation of results is …

Inspired by the feedforward multilayer perceptron (FF-MLP), decision tree (DT) and extreme
learning machine (ELM), a new classification model, called the subspace learning machine …

In this paper we present a comparative study of several methods that combine evolutionary
algorithms and local search to optimize multilayer perceptrons: A method that optimizes the …

Unlike conventional Neural Network (NN) algorithms that require the definition of the NN
architecture before learning starts, Constructive Neural Network (CoNN) algorithms enable …

In this thesis I investigate the modelling of cognitive development with constructivist neural
networks. I argue that the constructivist nature of development, that is, the building of a …