Steve Gunn
Steve Gunn
Professor of Electronics and Computer Science, University of Southampton
Verificeret mail på ecs.soton.ac.uk - Startside
Citeret af
Citeret af
Support vector machines for classification and regression
SR Gunn
ISIS technical report 14, 1998
Feature extraction
I Guyon, S Gunn, M Nikravesh, L Zadeh
Foundations and applications, 2006
Result analysis of the nips 2003 feature selection challenge
I Guyon, S Gunn, A Ben-Hur, G Dror
Advances in Neural Information Processing Systems, 545-552, 2004
Positron emission tomography compartmental models
RN Gunn, SR Gunn, VJ Cunningham
Journal of Cerebral Blood Flow & Metabolism 21 (6), 635-652, 2001
Band selection for hyperspectral image classification using mutual information
B Guo, SR Gunn, RI Damper, JDB Nelson
Geoscience and Remote Sensing Letters, IEEE 3 (4), 522-526, 2006
A robust snake implementation; a dual active contour
SR Gunn, MS Nixon
Pattern Analysis and Machine Intelligence, IEEE Transactions on 19 (1), 63-68, 1997
Linear spectral mixture models and support vector machines for remote sensing
M Brown, HG Lewis, SR Gunn
Geoscience and Remote Sensing, IEEE Transactions on 38 (5), 2346-2360, 2000
Positron Emission Tomography Compartmental Models: A Basis Pursuit Strategy for Kinetic Modeling
RN Gunn, SR Gunn, FE Turkheimer, JAD Aston, VJ Cunningham
Journal of Cerebral Blood Flow & Metabolism 22 (12), 1425-1439, 2002
Customizing kernel functions for SVM-based hyperspectral image classification
B Guo, SR Gunn, RI Damper, JDB Nelson
Image Processing, IEEE Transactions on 17 (4), 622-629, 2008
Support vector machines for optimal classification and spectral unmixing
M Brown, SR Gunn, HG Lewis
Ecological Modelling 120 (2), 167-179, 1999
A probabilistic framework for SVM regression and error bar estimation
JB Gao, SR Gunn, CJ Harris, M Brown
Machine Learning 46 (1-3), 71-89, 2002
Network performance assessment for neurofuzzy data modelling
SR Gunn, M Brown, KM Bossley
Advances in Intelligent Data Analysis Reasoning About Data, 313-323, 1997
A fast separability-based feature-selection method for high-dimensional remotely sensed image classification
B Guo, RI Damper, SR Gunn, JDB Nelson
Pattern Recognition 41 (5), 1653-1662, 2008
Identifying feature relevance using a random forest
J Rogers, S Gunn
Subspace, Latent Structure and Feature Selection, 173-184, 2006
Structural modelling with sparse kernels
SR Gunn, JS Kandola
Machine learning 48 (1-3), 137-163, 2002
On the discrete representation of the Laplacian of Gaussian
SR Gunn
Pattern Recognition 32 (8), 1463-1472, 1999
Machine learning can improve prediction of severity in acute pancreatitis using admission values of APACHE II score and C-reactive protein
CB Pearce, SR Gunn, A Ahmed, CD Johnson
Pancreatology 6 (1-2), 123-131, 2006
The relevance vector machine technique for channel equalization application
S Chen, SR Gunn, CJ Harris
IEEE Transactions on neural networks 12 (6), 1529-1532, 2001
Handwritten Chinese radical recognition using nonlinear active shape models
D Shi, SR Gunn, RI Damper
IEEE transactions on pattern analysis and machine intelligence 25 (2), 277-280, 2003
Decision feedback equaliser design using support vector machines
S Chen, S Gunn, CJ Harris
IEE Proceedings-Vision, Image and Signal Processing 147 (3), 213-219, 2000
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