Stephen P. Luttrell
Stephen P. Luttrell has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
C J Oliver
R. Signals & Radar Establ., Great Malvern, UK
1 shared publicationG C Pryde
University of Liverpool
1 shared publicationJ.A.S. Pritchard
Royal Signals and Radar Establishment, Malvern, UK
1 shared publicationL M Delves
University of Liverpool
1 shared publication
Latest Publications
- 24 August 2005
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 3, 1231-1238
Image compression Using A Neural Network
- 24 August 2005
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 3, 1231-1238
Image compression Using A Neural Network
- 26 November 2004
- Published by Taylor & Francis
Designing analyzable networks
- 24 June 2003
- Published by Springer Nature in Lecture Notes in Computer Science
- p. 217-226
A Markov Chain Approach to Multiple Classifier Fusion
- 24 June 2003
- Published by Springer Nature in Lecture Notes in Computer Science
- p. 217-226
A Markov Chain Approach to Multiple Classifier Fusion
- 22 June 2001
- Published by Springer Nature in Lecture Notes in Computer Science
- p. 319-328
A Self-Organising Approach to Multiple Classifier Fusion
- 22 June 2001
- Published by Springer Nature in Lecture Notes in Computer Science
- p. 319-328
A Self-Organising Approach to Multiple Classifier Fusion
- 1 January 2001
- Published by Institution of Engineering and Technology (IET)
Adaptive sensor fusion using stochastic vector quantisers
- 1 January 2001
- Published by Institution of Engineering and Technology (IET)
Adaptive sensor fusion using stochastic vector quantisers
- 15 May 2000
- Published by Cambridge University Press (CUP)
- p. 101-121
The Emergence of Dominance Stripes and Orientation Maps in a Network of Firing Neurons
- 15 May 2000
- Published by Cambridge University Press (CUP)
- p. 101-121
The Emergence of Dominance Stripes and Orientation Maps in a Network of Firing Neurons
- 1 March 1997
- Published by Taylor & Francis in Connection Science
- Vol. 9 (1) , 11-30
Self-organization of Multiple Winner-take-all Neural Networks
In this paper, analysis of the information content of discretely firing neurons in unsupervised neural networks is presented, where information is measured according to the network's ability to reconstruct its input from its output with minimum mean square Euclidean error. It is shown how this type of network can self-organize into multiple winner-take-all subnetworks, each of which tackles only a low-dimensional subspace of the input vector. This is a rudimentary example of a neural network that effectively subdivides a task into manageable subtasks.
- 1 March 1997
- Published by Taylor & Francis in Connection Science
- Vol. 9 (1) , 11-30
Self-organization of Multiple Winner-take-all Neural Networks
In this paper, analysis of the information content of discretely firing neurons in unsupervised neural networks is presented, where information is measured according to the network's ability to reconstruct its input from its output with minimum mean square Euclidean error. It is shown how this type of network can self-organize into multiple winner-take-all subnetworks, each of which tackles only a low-dimensional subspace of the input vector. This is a rudimentary example of a neural network that effectively subdivides a task into manageable subtasks.
- 1 January 1997
- Published by Institution of Engineering and Technology (IET)
Partitioned mixture distributions: the dynamical case
- 1 January 1997
- Published by Institution of Engineering and Technology (IET)
Partitioned mixture distributions: the dynamical case