Steve R. Gunn

Chris Lovell, Gareth Jones, Klaus-Peter Zauner, Steve R. Gunn: Exploration and Exploitation with Insufficient Resources. Journal of Machine Learning Research - Proceedings Track 26: 37-61 (2012)

Gareth Jones, Chris Lovell, Steve R. Gunn, Hywel Morgan, Klaus-Peter Zauner: Enabling the discovery of computational characteristics of enzyme dynamics. IEEE Congress on Evolutionary Computation 2012: 1-8

Chris Lovell, Steve R. Gunn: Towards improved theoretical problems for autonomous discovery. IJCNN 2012: 1-8

Chris Lovell, Gareth Jones, Steve R. Gunn, Klaus-Peter Zauner: Autonomous Experimentation. Journal of Machine Learning Research - Proceedings Track 16: 141-155 (2011)

Sasan Mahmoodi, Steve R. Gunn: Snake based unsupervised texture segmentation using Gaussian Markov Random Field Models. ICIP 2011: 3353-3356

Sasan Mahmoodi, Steve R. Gunn: Scale Space Smoothing, Image Feature Extraction and Bessel Filters. SCIA 2011: 625-634

Sándor Szedmák, Yizhao Ni, Steve R. Gunn: Maximum Margin Learning with Incomplete Data: Learning Networks instead of Tables. Journal of Machine Learning Research - Proceedings Track 11: 96-102 (2010)

Chris Lovell, Gareth Jones, Steve R. Gunn, Klaus-Peter Zauner: An Artificial Experimenter for Enzymatic Response Characterisation. Discovery Science 2010: 42-56

Chris Lovell, Gareth Jones, Steve R. Gunn, Klaus-Peter Zauner: Characterising Enzymes for Information Processing: Towards an Artificial Experimenter. UC 2010: 81-92

James D. B. Nelson, Robert I. Damper, Steve R. Gunn, Baofeng Guo: A signal theory approach to support vector classification: The sinc kernel. Neural Networks 22(1): 49-57 (2009)

Charanpal Dhanjal, Steve R. Gunn, John Shawe-Taylor: Efficient Sparse Kernel Feature Extraction Based on Partial Least Squares. IEEE Trans. Pattern Anal. Mach. Intell. 31(8): 1347-1361 (2009)

Amit Acharyya, Koushik Maharatna, Bashir M. Al-Hashimi, Steve R. Gunn: Memory Reduction Methodology for Distributed-Arithmetic-Based DWT/IDWT Exploiting Data Symmetry. IEEE Trans. on Circuits and Systems 56-II(4): 285-289 (2009)

James D. B. Nelson, Robert I. Damper, Steve R. Gunn, Baofeng Guo: Signal theory for SVM kernel design with applications to parameter estimation and sequence kernels. Neurocomputing 72(1-3): 15-22 (2008)

Baofeng Guo, Robert I. Damper, Steve R. Gunn, James D. B. Nelson: A fast separability-based feature-selection method for high-dimensional remotely sensed image classification. Pattern Recognition 41(5): 1653-1662 (2008)

Baofeng Guo, Steve R. Gunn, Robert I. Damper, James D. B. Nelson: Customizing Kernel Functions for SVM-Based Hyperspectral Image Classification. IEEE Transactions on Image Processing 17(4): 622-629 (2008)

Jianqiang Yang, Steve R. Gunn: Exploiting Uncertain Data in Support Vector Classification. KES (3) 2007: 148-155

Craig Saunders, Marko Grobelnik, Steve R. Gunn, John Shawe-Taylor (Eds.): Subspace, Latent Structure and Feature Selection, Statistical and Optimization, Perspectives Workshop, SLSFS 2005, Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers. Lecture Notes in Computer Science 3940, Springer 2006, isbn 3-540-34137-4

Jeremy Rogers, Steve R. Gunn: Identifying Feature Relevance Using a Random Forest. SLSFS 2005: 173-184

Jeremy Rogers, Steve R. Gunn: Ensemble Algorithms for Feature Selection. Deterministic and Statistical Methods in Machine Learning 2004: 180-198

Ahmad Al-Mazeed, Mark S. Nixon, Steve R. Gunn: Classifiers Combination for Improved Motion Segmentation. ICIAR (2) 2004: 363-371

Isabelle Guyon, Steve R. Gunn, Asa Ben-Hur, Gideon Dror: Result Analysis of the NIPS 2003 Feature Selection Challenge. NIPS 2004

Junbin Gao, Steve R. Gunn, Chris J. Harris: Mean field method for the support vector machine regression. Neurocomputing 50: 391-405 (2003)

Junbin Gao, Steve R. Gunn, Chris J. Harris: SVM regression through variational methods and its sequential implementation. Neurocomputing 55(1-2): 151-167 (2003)

Daming Shi, Robert I. Damper, Steve R. Gunn: An Approach to Off-Line Handwritten Chinese Character Recognition Based on Hierarchical Radical Decomposition. Journal of Quantitative Linguistics 10(1): 41-69 (2003)

Daming Shi, Steve R. Gunn, Robert I. Damper: Handwritten Chinese Radical Recognition Using Nonlinear Active Shape Models. IEEE Trans. Pattern Anal. Mach. Intell. 25(2): 277-280 (2003)

Daming Shi, Robert I. Damper, Steve R. Gunn: Offline handwritten Chinese character recognition by radical decomposition. ACM Trans. Asian Lang. Inf. Process. 2(1): 27-48 (2003)

Ahmad Al-Mazeed, Mark S. Nixon, Steve R. Gunn: Fusing Complementary Operators to Enhance Foreground/Background Segmentation. BMVC 2003: 1-10

Geok See Ng, Daming Shi, Steve R. Gunn, Robert I. Damper: Nonlinear Active Handwriting Models and Their Applications to Handwritten Chinese Radical Recognition. ICDAR 2003: 534-

Junbin Gao, Steve R. Gunn, Chris J. Harris, Martin Brown: A Probabilistic Framework for SVM Regression and Error Bar Estimation. Machine Learning 46(1-3): 71-89 (2002)

Steve R. Gunn, Jaz S. Kandola: Structural Modelling with Sparse Kernels. Machine Learning 48(1-3): 137-163 (2002)

Daming Shi, Steve R. Gunn, Robert I. Damper: Handwritten Chinese character recognition using nonlinear active shape models and the Viterbi algorithm. Pattern Recognition Letters 23(14): 1853-1862 (2002)

Sheng Chen, Steve R. Gunn, Chris J. Harris: Errata to "The relevance vector machine technique for channel equalization application". IEEE Transactions on Neural Networks 13(4): 1024 (2002)

Junbin Gao, Steve R. Gunn, Jaz S. Kandola: Adapting Kernels by Variational Approach in SVM. Australian Joint Conference on Artificial Intelligence 2002: 395-406

Junbin Gao, Chris J. Harris, Steve R. Gunn: On a Class of Support Vector Kernels Based on Frames in Function Hilbert Spaces. Neural Computation 13(9): 1975-1994 (2001)

Sheng Chen, Steve R. Gunn, Chris J. Harris: The relevance vector machine technique for channel equalization application. IEEE Transactions on Neural Networks 12(6): 1529-1532 (2001)

Daming Shi, Steve R. Gunn, Robert I. Damper: A Radical Approach to Handwritten Chinese Character Recognition Using Active Handwriting Models. CVPR (1) 2001: 670-675

Adam I. Wilmer, Tania Stathaki, Steve R. Gunn, Robert I. Damper: Texture analysis with the Volterra model using conjugate gradient optimisation. ESANN 2001: 211-216

Daming Shi, Steve R. Gunn, Robert I. Damper: Active Radical Modeling for Handwritten Chinese Characters. ICDAR 2001: 236-240

Jun L. Chen, Steve R. Gunn, Mark S. Nixon, Roger N. Gunn: Markov Random Field Models for Segmentation of PET Images. IPMI 2001: 468-474

Robert I. Damper, Steve R. Gunn, Mathew O. Gore: Extracting Phonetic Knowledge from Learning Systems: Perceptrons, Support Vector Machines and Linear Discriminants. Appl. Intell. 12(1-2): 43-62 (2000)

Martin Brown, Hugh G. Lewis, Steve R. Gunn: Linear spectral mixture models and support vector machines for remote sensing. IEEE T. Geoscience and Remote Sensing 38(5): 2346-2360 (2000)

Steve R. Gunn: On the discrete representation of the Laplacian of Gaussian. Pattern Recognition 32(8): 1463-1472 (1999)

Robert I. Damper, Steve R. Gunn: Learning phonetic distinctions from speech signals. EUROSPEECH 1999

Steve R. Gunn, Mark S. Nixon: Global and Local Active Contours for Head Boundary Extraction. International Journal of Computer Vision 30(1): 43-54 (1998)

Steve R. Gunn: Edge Detection Error in the Discrete Laplacian of Gaussian. ICIP (2) 1998: 515-519

Robert I. Damper, Steve R. Gunn: On the learnability of the voicing contrast for initial stops. ICSLP 1998

Steve R. Gunn, Mark S. Nixon: A Robust Snake Implementation; A Dual Active Contour. IEEE Trans. Pattern Anal. Mach. Intell. 19(1): 63-68 (1997)

Steve R. Gunn, Martin Brown, Kev M. Bossley: Network Performance Assessment for Neurofuzzy Data Modelling. IDA 1997: 313-323

Steve R. Gunn, Mark S. Nixon: Improving snake performance via a dual active contour. CAIP 1995: 600-605

Steve R. Gunn, Mark S. Nixon: A Model Based Dual Active Contour. BMVC 1994: 1-10