A common framework for architectures combining multiple vector-quantization of the input space with memory look-up operations is proposed. Properties of the model are discussed and, in particular, a close relationship with basis functions networks (such as RBFs and kernel regression networks) is establishe
This paper presents an overview of novel networking strategies for neural networks which significant...
Neural network is a web of million numbers of inter-connected neurons which executes parallel proces...
This paper presents a probabilistic approach based on collisions to assess the storage capacity of R...
A generalization of a class of neural network architectures based on a multiple quantization of inpu...
A new architecture for networks of RAM-based Boolean neurons is presented which, whilst retaining le...
A new architecture for networks of RAM-based Boolean neurons is presented which, whilst retaining le...
<p>Architecture (a) corresponds to the algorithm of learning and recall as described in the text. In...
Abstract: Usually, generalization is considered as a function of learning from a set of examples. In...
Memory Networks are models equipped with a storage component where information can generally be writ...
Originally, artificial neural networks were built from biologically inspired units called perceptron...
Abstract-This paper describes a memory-based network that provides estimates of continuous variables...
This paper introduces a new model of associative memory, capable of both binary and continuous-value...
Multilayer Perceptrons (MLP, Werbos 1974, Rumelhart et al. 1986) and Radial Basis Function Networks ...
Abstract: A framework for Similarity-Based Methods (SBMs) includes many classification models as spe...
In the paper, artificial neural networks and their various concepts in pattern recognition and signa...
This paper presents an overview of novel networking strategies for neural networks which significant...
Neural network is a web of million numbers of inter-connected neurons which executes parallel proces...
This paper presents a probabilistic approach based on collisions to assess the storage capacity of R...
A generalization of a class of neural network architectures based on a multiple quantization of inpu...
A new architecture for networks of RAM-based Boolean neurons is presented which, whilst retaining le...
A new architecture for networks of RAM-based Boolean neurons is presented which, whilst retaining le...
<p>Architecture (a) corresponds to the algorithm of learning and recall as described in the text. In...
Abstract: Usually, generalization is considered as a function of learning from a set of examples. In...
Memory Networks are models equipped with a storage component where information can generally be writ...
Originally, artificial neural networks were built from biologically inspired units called perceptron...
Abstract-This paper describes a memory-based network that provides estimates of continuous variables...
This paper introduces a new model of associative memory, capable of both binary and continuous-value...
Multilayer Perceptrons (MLP, Werbos 1974, Rumelhart et al. 1986) and Radial Basis Function Networks ...
Abstract: A framework for Similarity-Based Methods (SBMs) includes many classification models as spe...
In the paper, artificial neural networks and their various concepts in pattern recognition and signa...
This paper presents an overview of novel networking strategies for neural networks which significant...
Neural network is a web of million numbers of inter-connected neurons which executes parallel proces...
This paper presents a probabilistic approach based on collisions to assess the storage capacity of R...