Although artificial neural networks have occasionally been used for Quantitative Structure-Activity/Property Relationship (QSAR/QSPR)studies in the past, the literature has of late been dominated by other machine learning techniques such as random forests. However, a variety of new neural net techniques along with successful applications in other domains have renewed interest in network approac...

The go-playing program Honte is described. It uses neural nets together with more conventional AI-methods like alpha-beta search. A neural net is trained by supervised learning to imitate local shapes made in a database of expert games. A second net is trained to estimate the safety of groups by self play using TD(λ)learning. A third net is trained to estimate territorial potential of unoccupie...

A neural net based development of multi-adjoint logic programming is presented. Transformation rules carry programs into neural networks, where truth-values of rules relate to output of neurons, truth-values of facts represent input, and network functions are determined by a set of general operators; the output of the net being the values of propositional variables under its minimal model. Some...

We present a neural net based implementation of propositional [0, 1]-valued multiadjoint logic programming. The implementation needs some preprocessing of the initial program to transform it in a homogeneous program; then, transformation rules carry programs into neural networks, where truth-values of rules relate to output of neurons, truth-values of facts represent input, and network function...

This paper presents the use of neural net hierarchy for feature extraction in ASR. The recently proposed Bottle-Neck feature extraction is extended and used in hierarchical structures to enhance the discriminative property of the features. Although many ways of hierarchical classification/feature extraction have been proposed, we restricted ourselves to use the outputs of the first stage neural...

Proposes a neural network based invariant character recognition system using double backpropagation network. The model consists of two parts. The first is a preprocessor which is intended to produce a translation, rotation and scale invariant representation of the input pattern. The second is a neural net classifier. The outputs produced by the preprocessor at the first stage are classified by ...