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Clustering by document concepts is a powerful way of retrieving information from a large number of documents. This task in general does not make any assumption on the data distribution. In this paper, for this task we propose a new competitive Self-Organising (SOM) model, namely the Dynamic Adaptive Self-Organising Hybrid model (DASH). The features of DASH(More)
We present a neurobiologically motivated model of a neuron with active dendrites and dynamic synapses, and a training algorithm which builds upon single spike-timing-dependent synaptic plasticity derived from neurophysiological evidence. We show that in the presence of a moderate level of noise, the plasticity rule can be extended from single to multiple(More)
This paper describes techniques for integrating neural networks and symbolic components into powerful hybrid systems. Neural networks have unique processing characteristics that enable tasks to be performed that would be diicult or intractable for a symbolic rule-based system. However, a stand-alone neural network requires an interpretation either by a h(More)
Preface The purpose of this book is to present a collection of papers that represents a broad spectrum of current research in learning methods for natural language processing, and to advance the state of the art in language learning and artiicial intelligence. The book should bridge a gap between several areas that are usually discussed separately,(More)
Presented is a model of an integrate-and-fire neuron with active den-drites and a spike-timing dependent Hebbian learning rule. The learning algorithm effectively trains the neuron when responding to several types of temporal encoding schemes: temporal code with single spikes, spike bursts and phase coding. The neuron model and learning algorithm are tested(More)
In this paper we describe a new approach for learning dialog act processing. In this approach we integrate a symbolic semantic segmentation parser with a learning dialog act network. In order to support the unforeseeable errors and variations of spoken language we have concentrated on robust data-driven learning. This approach already compares favorably(More)
1 Abstract Recently there has been a lot of interest in the extraction of symbolic rules from neural networks. The work described in this paper is concerned with an evaluation and comparison of the accuracy and complexity of symbolic rules extracted from radial basis function networks and multi-layer perceptrons. Here we examine the ability of rule(More)