Abstract
It is well-known that the architecture of a neural network (NN) plays a crucial role on its generalisation capabilities. While conventional NN training algorithms assume a pre-defined NN architecture before training starts, constructive neural network (CoNN) algorithms define the architecture of the NN along with its training. This paper describes the proposal of a new two-class CoNN algorithm named OffTiling, suitable for continuous valued domains and slightly inspired by two other CoNN algorithms found in the literature, the Offset, designed for learning in Boolean domains and the two-class CoNN Tiling algorithm. A comparative analysis of the OffTiling versus four other CoNN algorithms, namely the Pyramid, Tiling, PTI and the TilingLike in ten knowledge domains is presented, showing that the OffTiling can be a competitive algorithm as far as the performance of the network is concerned.
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