PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

EPrints submitted by Steve Gunn

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Number of EPrints submitted by this user: 11

Result Analysis of the NIPS 2003 Feature Selection Challenge
Isabelle Guyon, Steve Gunn, Asa Ben-Hur and Gideon Dror
In: NIPS 2004, 13-16 Dec 2004, Vancouver, Canada.

Ensemble Algorithms for Feature Selection
Jeremy Rogers and Steve Gunn
In: Machine Learning Workshop, 7-10 Sep 2004, Sheffield, U.K..

Input Uncertainty in Support Vector Machines
Jianqiang Yang and Steve Gunn
In: Machine Learning Workshop, 7-10 Sep 2004, Sheffield, U.K..

Cooperative Information Sharing to Improve Distributed Learning
Partha Dutta, Srinandan Dasmahapatra, Steve Gunn, Nick Jennings and Luc Moreau
In: AAMAS 2004 Workshop on Learning and Evolution in Agent-Based Systems, New York, USA(2004).

Classifier Combination for Improved Motion Segmentation
Ahmad Al-Mazeed, Mark Nixon and Steve Gunn
In: International Conference on Image Analysis and Recognition, Porto, Portugal(2004).

A signal theory approach to support vector classification: The sinc kernel
J. D. B. Nelson, Bob Damper, Steve Gunn and B. Guo
Neural Networds Volume 1, Number 22, pp. 49-57, 2009.

Customizing kernel functions for SVM-based hyperspectral image classification
B. Guo, Steve Gunn, Bob Damper and J. D. B. Nelson
IEEE Transactions on Image Processing Volume 17, Number 4, pp. 622-629, 2008.

Efficient Sparse Kernel Feature Extraction Based on Partial Least Squares.
Charanpal Dhanjal, Steve Gunn and John Shawe-Taylor
IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 99, Number 1, 2008.

A fast separability-based feature selection method for high-dimensional remotely-sensed image classification.
B. Guo, Bob Damper, Steve Gunn and J. Nelson
Pattern Recognition Volume 41, Number 5, pp. 1670-1679, 2008.

Memory Reduction Methodology for Distributed Arithmetic Based DWT/ IDWT.
A. Acharyya, K. Maharatna, B. Al-Hashimi and Steve Gunn
IEEE Transactions on Circuits and Systems - II 2008.

Signal theory for SVM kernel design with applications to parameter estimation and sequence kernels.
J. D. B. Nelson, Bob Damper, Steve Gunn and B. Guo
Neurocomputing Volume 72, Number 1-3, pp. 15-22, 2008.