A new architecture for networks of RAM-based Boolean neurons is presented which, whilst retaining learning and generalisation properties possessed by existing network architectures, allows for a regular treatment of specialisation and generalisation with strong convergence properties. The network architecture provides the basis for a pattern recognition system capable of application in a practical environment
This paper presents a new neural network structure and namely node-to-node-link neural network (N-N-...
Until now neural networks have been used for classifying unstructured patterns and sequences, Howeve...
A multi-layered neural assembly is developed which has the capability of learning arbitrary Boolean ...
A new architecture for networks of RAM-based Boolean neurons is presented which, whilst retaining le...
We discuss the adaptable Boolean net neural paradigm together with its learning and generalization p...
A generalization of a class of neural network architectures based on a multiple quantization of inpu...
This paper presents an overview of novel networking strategies for neural networks which significant...
The most commonly used neural network models are not well suited to direct digital implementations b...
A common framework for architectures combining multiple vector-quantization of the input space with ...
This paper analyses the parallel implementation using networks of transputers of a neural structure ...
We propose a multi-layered Neural Network architecture which enables a knowledge based segmentation ...
In this article a new generalized feedforward neural network (GFNN) architecture for pattern classif...
In this paper we investigate multi-layer perceptron networks in the task domain of Boolean functions...
This paper reviews some of the recent results in applying the theory of Probably Approximately Corre...
Abstract: A framework for Similarity-Based Methods (SBMs) includes many classification models as spe...
This paper presents a new neural network structure and namely node-to-node-link neural network (N-N-...
Until now neural networks have been used for classifying unstructured patterns and sequences, Howeve...
A multi-layered neural assembly is developed which has the capability of learning arbitrary Boolean ...
A new architecture for networks of RAM-based Boolean neurons is presented which, whilst retaining le...
We discuss the adaptable Boolean net neural paradigm together with its learning and generalization p...
A generalization of a class of neural network architectures based on a multiple quantization of inpu...
This paper presents an overview of novel networking strategies for neural networks which significant...
The most commonly used neural network models are not well suited to direct digital implementations b...
A common framework for architectures combining multiple vector-quantization of the input space with ...
This paper analyses the parallel implementation using networks of transputers of a neural structure ...
We propose a multi-layered Neural Network architecture which enables a knowledge based segmentation ...
In this article a new generalized feedforward neural network (GFNN) architecture for pattern classif...
In this paper we investigate multi-layer perceptron networks in the task domain of Boolean functions...
This paper reviews some of the recent results in applying the theory of Probably Approximately Corre...
Abstract: A framework for Similarity-Based Methods (SBMs) includes many classification models as spe...
This paper presents a new neural network structure and namely node-to-node-link neural network (N-N-...
Until now neural networks have been used for classifying unstructured patterns and sequences, Howeve...
A multi-layered neural assembly is developed which has the capability of learning arbitrary Boolean ...