Steve R. Gunn

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A Stochastic Gradient Method with Biased Estimation for Faster Nonconvex Optimization pdf

Jia Bi, Steve R. Gunn, 2019, PRICAI.

Support vector machines for spectral unmixing

Martin Brown, Hugh G. Lewis, Steve R. Gunn, 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

Result Analysis of the NIPS 2003 Feature Selection Challenge

Steve R. Gunn, Isabelle Guyon, Gideon Dror, 2004, NIPS.

Hardware efficient fixed-point VLSI architecture for 2D Kurtotic FastICA

Amit Acharyya, Bashir M. Al-Hashimi, Steve R. Gunn, 2009, 2009 European Conference on Circuit Theory and Design.

A signal theory approach to support vector classification: The sinc kernel

Robert I. Damper, Steve R. Gunn, James D. B. Nelson, 2009, Neural Networks.

Fusing Complementary Operators to Enhance Foreground/Background Segmentation

Steve R. Gunn, Mark S. Nixon, Ahmad Al-Mazeed, 2003, BMVC.

On the discrete representation of the Laplacian of Gaussian

Steve R. Gunn, S. Gunn, 1999, Pattern Recognit..

Considerations on extended feature vectors in automatic face recognition

Steve R. Gunn, Mark S. Nixon, L. S. Ng, 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

SVM regression through variational methods and its sequential implementation

Junbin Gao, Steve R. Gunn, Christopher J. Harris, 2003, Neurocomputing.

Support vector machines for optimal classification and spectral unmixing

Martin Brown, Hugh G. Lewis, Steve R. Gunn, 1999 .

Cooperative Information Sharing to Improve Distributed Learning

Nicholas R. Jennings, Luc Moreau, Steve R. Gunn, 2004 .

Modelling with Support Vector Machines

Steve R. Gunn, S. Gunn, 2000 .

Regression with Input-dependent Noise: a Relevance Vector Machine Treatment

Junbin Gao, Martin Brown, Chris J. Harris, 2001 .

Signal theory for SVM kernel design with applications to parameter estimation and sequence kernels

Robert I. Damper, Steve R. Gunn, James D. B. Nelson, 2008, Neurocomputing.

The relevance vector machine technique for channel equalization application

Sheng Chen, Steve R. Gunn, Christopher J. Harris, 2001, IEEE Trans. Neural Networks.

Coarse-Grained Online Monitoring of BTI Aging by Reusing Power-Gating Infrastructure

Bashir M. Al-Hashimi, Daniele Rossi, Steve R. Gunn, 2017, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

Lightweight PUF-Based Authentication Protocol for IoT Devices

Steve R. Gunn, Basel Halak, Yildiran Yilmaz, 2018, 2018 IEEE 3rd International Verification and Security Workshop (IVSW).

Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection

Marko Grobelnik, John Shawe-Taylor, Steve R. Gunn, 2005 .

Markov Random Field Models for Segmentation of PET Images

Steve R. Gunn, Mark S. Nixon, Roger N. Gunn, 2001, IPMI.

Modeling the acoustic-to-auditory transformation for stop consonant-vowel syllables

Robert I. Damper, Steve R. Gunn, 2001 .

Extracting Phonetic Knowledge from Learning Systems: Perceptrons, Support Vector Machines and Linear Discriminants

Robert I. Damper, Steve R. Gunn, Mathew O. Gore, 2004, Applied Intelligence.

Characterising Enzymes for Information Processing: Towards an Artificial Experimenter

Klaus-Peter Zauner, Steve R. Gunn, Chris Lovell, 2010, UC.

Customizing Kernel Functions for SVM-Based Hyperspectral Image Classification

Robert I. Damper, Steve R. Gunn, James D. B. Nelson, 2008, IEEE Transactions on Image Processing.

Feature Extraction - Foundations and Applications

Masoud Nikravesh, Steve R. Gunn, Isabelle Guyon, 2006, Feature Extraction.

Ranking algorithms for implicit feedback

Alex Po Leung, Kitsuchart Pasupa, Peter Auer, 2009 .

Offline handwritten Chinese character recognition by radical decomposition

Daming Shi, Robert I. Damper, Steve R. Gunn, 2003, TALIP.

Assessing the Stability of Advanced Transparent Modelling Techniques

Steve R. Gunn, J. S. Kandola, J. Kandola, 2000 .

Neurofuzzy Systems Modelling: A Transparent Approach

Martin Brown, Chris J. Harris, Steve R. Gunn, 1997 .

A Dual Active Contour Incorporating Parametric Shape Description

Steve R. Gunn, Nixon, 1994 .

A radical approach to handwritten Chinese character recognition using active handwriting models

Daming Shi, Robert I. Damper, Steve R. Gunn, 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

Nonlinear active handwriting models and their applications to handwritten Chinese radical recognition

Daming Shi, Robert I. Damper, Steve R. Gunn, 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

Estimation of sub-pixel land cover using support vector methods

Martin Brown, Hugh G. Lewis, Steve R. Gunn, 1999 .

Network Performance Assessment for Neurofuzzy Data Modelling

Martin Brown, Steve R. Gunn, Kev M. Bossley, 1997, IDA.

An Artificial Experimenter for Enzymatic Response Characterisation

Klaus-Peter Zauner, Steve R. Gunn, Chris Lovell, 2010, Discovery Science.

Data driven knowledge extraction of materials properties

Steve R. Gunn, Ian Sinclair, J. S. Kandola, 1999, Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials. IPMM'99 (Cat. No.99EX296).

Towards Pareto Descent Directions in Sampling Experts for Multiple Tasks in an On-Line Learning Paradigm

Steve R. Gunn, Shaona Ghosh, Chris Lovell, 2013, AAAI Spring Symposium: Lifelong Machine Learning.

Subspace, Latent Structure and Feature Selection techniques

Marko Grobelnik, John Shawe-Taylor, John Shawe-Taylor, 2006 .

Classifiers Combination for Improved Motion Segmentation

Steve R. Gunn, Mark S. Nixon, Ahmad Al-Mazeed, 2004, ICIAR.

Enabling the discovery of computational characteristics of enzyme dynamics

Klaus-Peter Zauner, Steve R. Gunn, Hywel Morgan, 2012, 2012 IEEE Congress on Evolutionary Computation.

Edge detection error in the discrete Laplacian of Gaussian

Steve R. Gunn, 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

Adaptive Numerical Modelling of Commercial Aluminium Plate Performance

Steve R. Gunn, Ian Sinclair, Philippa A.S. Reed, 2000 .

Regression models for classification to enhance interpretability

Chris J. Harris, Steve R. Gunn, Philippa A.S. Reed, 2001 .

An Approach to Off-Line Handwritten Chinese Character Recognition Based on Hierarchical Radical Decomposition

Daming Shi, Robert I. Damper, Steve R. Gunn, 2003, J. Quant. Linguistics.

Snake head boundary extraction using global and local energy minimisation

Steve R. Gunn, Mark S. Nixon, S. Gunn, 1996, Proceedings of 13th International Conference on Pattern Recognition.

Comparison of Modelling Techniques in the Analysis of Commercial Materials Data

Steve R. Gunn, Ian Sinclair, Philippa A.S. Reed, 2002 .

Active radical modeling for handwritten Chinese characters

Daming Shi, Robert I. Damper, Steve R. Gunn, 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition.

Exploration and Exploitation with Insufficient Resources

Klaus-Peter Zauner, Steve R. Gunn, Chris Lovell, 2011, ICML On-line Trading of Exploration and Exploitation.

Memory Reduction Methodology for Distributed-Arithmetic-Based DWT/IDWT Exploiting Data Symmetry

Amit Acharyya, Bashir M. Al-Hashimi, Steve R. Gunn, 2009, IEEE Transactions on Circuits and Systems II: Express Briefs.

Sparse Deep Neural Network Optimization for Embedded Intelligence

Jia Bi, Steve R. Gunn, 2020, Int. J. Artif. Intell. Tools.

Band Selection for Hyperspectral Image Classification Using Mutual Information

Robert I. Damper, Steve R. Gunn, James D. B. Nelson, 2006, IEEE Geoscience and Remote Sensing Letters.

Identifying Feature Relevance Using a Random Forest

Steve R. Gunn, Jeremy Rogers, S. Gunn, 2005, SLSFS.

Intelligent data modelling using neurofuzzy algorithms

Martin Brown, Chris J. Harris, Steve R. Gunn, 1997 .

Sparse kernel methods

Steve R. Gunn, S. Gunn, 2003 .

Approaches to classification of imbalanced data - a case study on automotive materials

Chris J. Harris, Steve R. Gunn, K. K. Lee, 2000 .

Ensemble Algorithms for Feature Selection

Steve R. Gunn, Jeremy Rogers, S. Gunn, 2004, Deterministic and Statistical Methods in Machine Learning.

Control Sensitivity SVM for Imbalanced Data A Case Study on Automotive Material

Steve R. Gunn, Philippa A.S. Reed, Chris J. Harris, 2001 .

Exploration and Exploitation in an Artificial Experimenter

Klaus-Peter Zauner, Steve R. Gunn, Chris Lovell, 2011 .

Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)

Masoud Nikravesh, Steve R. Gunn, Isabelle Guyon, 2006 .

Approaches to imbalanced data for classification: a case study

Chris J. Harris, Steve R. Gunn, K. K. Lee, 2001 .

Managing a Large network of excellence: Case Study of the PASCAL Network

John Shawe-Taylor, Steve R. Gunn, Nicolò Cesa-Bianchi, 2007 .

Mean field method for the support vector machine regression

Junbin Gao, Steve R. Gunn, Christopher J. Harris, 2003, Neurocomputing.

Learning phonetic distinctions from speech signals

Robert I. Damper, Steve R. Gunn, 1999, EUROSPEECH.

Learning to rank images from eye movements

Kitsuchart Pasupa, Samuel Kaski, Steve R. Gunn, 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

Subspace, Latent Structure and Feature Selection: Statistical and Optimization Perspectives Workshop, SLSFS 2005Bohinj, Slovenia, February 23-25, 2005 ... Papers (Lecture Notes in Computer Science)

Marko Grobelnik, John Shawe-Taylor, Steve R. Gunn, 2006 .

Exploiting Uncertain Data in Support Vector Classification

Jianqiang Yang, Steve R. Gunn, 2007, KES.

SUPANOVA: a sparse, transparent modelling approach

Martin Brown, Steve R. Gunn, S. Gunn, 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

Snake based unsupervised texture segmentation using Gaussian Markov Random Field Models

Steve R. Gunn, Sasan Mahmoodi, 2011, 2011 18th IEEE International Conference on Image Processing.

A model-based image segmentation framework using labeled and unlabeled data

Steve R. Gunn, Nixon, J. L. Chen, 2001 .

Autonomous Experimentation: Active Learning for Enzyme Response Characterisation

Klaus-Peter Zauner, Steve R. Gunn, Chris Lovell, 2011 .

Adapting Kernels by Variational Approach in SVM

Junbin Gao, Steve R. Gunn, Jaz S. Kandola, 2002, Australian Joint Conference on Artificial Intelligence.

Using MRI to image the moving vocal tract during speech

Steve R. Gunn, John N. Carter, Christine H. Shadle, 1997, EUROSPEECH.

Project Special Brew: Supercomputing on a budget

Steve R. Gunn, 2001 .

A Robust Snake Implementation; A Dual Active Contour

Steve R. Gunn, Mark S. Nixon, S. Gunn, 1997, IEEE Trans. Pattern Anal. Mach. Intell..

Linear spectral mixture models and support vector machines for remote sensing

Martin Brown, Hugh G. Lewis, Steve R. Gunn, 2000, IEEE Trans. Geosci. Remote. Sens..

Global and Local Active Contours for Head Boundary Extraction

Steve R. Gunn, Mark S. Nixon, 2004, International Journal of Computer Vision.

Improving hyperspectral band selection by constructing an estimated reference map

Robert I. Damper, Steve R. Gunn, Baofeng Guo, 2014 .

Handwritten Chinese Radical Recognition Using Nonlinear Active Shape Models

Daming Shi, Robert I. Damper, Steve R. Gunn, 2003, IEEE Trans. Pattern Anal. Mach. Intell..

A dual active contour for head boundary extraction

Steve R. Gunn, Mark S. Nixon, 1994 .

Design and Analysis of the NIPS2003 Challenge

Steve R. Gunn, Isabelle Guyon, Gideon Dror, 2006, Feature Extraction.

On a Class of Support Vector Kernels Based on Frames in Function Hilbert Spaces

Junbin Gao, Steve R. Gunn, Christopher J. Harris, 2001, Neural Computation.

Towards improved theoretical problems for autonomous discovery

Steve R. Gunn, Chris Lovell, S. Gunn, 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

Errata to "The relevance vector machine technique for channel equalization application"

Sheng Chen, Steve R. Gunn, Christopher J. Harris, 2002, IEEE Trans. Neural Networks.

Sparse Deep Neural Networks for Embedded Intelligence

Jia Bi, Steve R. Gunn, S. Gunn, 2018, 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI).

Autonomous Experimentation: Coupling Active Learning with Computer Controlled Microfluidics (abstract)

Klaus-Peter Zauner, Steve R. Gunn, Chris Lovell, 2010 .

On the learnability of the voicing contrast for initial stops

Robert I. Damper, Steve R. Gunn, 1998, ICSLP.

Product Lifecycle De-trending for Sales Forecasting

Steve R. Gunn, Albert F. H. M. Lechner, 2020, COMPLEXIS.

Autonomous Experimentation

Klaus-Peter Zauner, Steve R. Gunn, Chris Lovell, 2011, Active Learning and Experimental Design @ AISTATS.

Texture analysis with the Volterra model using conjugate gradient optimisation

Tania Stathaki, Robert I. Damper, Steve R. Gunn, 2001, ESANN.

Adaptive sampling in context-aware systems: A machine learning approach

Wendy Hall, Geoff V. Merrett, Nigel Shadbolt, 2012 .

Efficient Sparse Kernel Feature Extraction Based on Partial Least Squares

John Shawe-Taylor, Steve R. Gunn, Charanpal Dhanjal, 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Subspace, Latent Structure and Feature Selection, Statistical and Optimization, Perspectives Workshop, SLSFS 2005, Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers

Marko Grobelnik, John Shawe-Taylor, Steve R. Gunn, 2006, SLSFS.

Structural Modelling with Sparse Kernels

Steve R. Gunn, Jaz S. Kandola, J. Kandola, 2002, Machine Learning.

A Case Study of SVM Extension Techniques on classification of Imbalanced Data

Chris J. Harris, Steve R. Gunn, Philippa A.S. Reed, 2001 .

Scale Space Smoothing, Image Feature Extraction and Bessel Filters

Steve R. Gunn, Sasan Mahmoodi, 2011, SCIA.

VLSI architecture for fetal ECG extraction for personalized healthcare application within resource constrained environmentT

Amit Acharyya, Steve R. Gunn, Koushik Maharatna, 2010 .

Control Sensitivity SVM for Imbalanced Data

Chris J. Harris, Steve R. Gunn, K. K. Lee, 2001 .

Dynamic Cluster based Markov Model for Demand Forecasting

Steve R. Gunn, Albert F. H. M. Lechner, 2019 .

Improving snake performance via a dual active contour

Steve R. Gunn, Mark S. Nixon, 1995, CAIP.

A Model Based Dual Active Contour

Steve R. Gunn, Mark S. Nixon, S. Gunn, 1994, BMVC.

A Probabilistic Framework for SVM Regression and Error Bar Estimation

Junbin Gao, Martin Brown, Steve R. Gunn, 2002, Machine Learning.

A Supervised Method for PET Reference Region Extraction

Steve R. Gunn, Roger N. Gunn, Nixon, 2000 .

Handwritten Chinese character recognition using nonlinear active shape models and the Viterbi algorithm

Daming Shi, Robert I. Damper, Steve R. Gunn, 2002, Pattern Recognit. Lett..

A fast separability-based feature-selection method for high-dimensional remotely sensed image classification

Robert I. Damper, Steve R. Gunn, James D. B. Nelson, 2008, Pattern Recognit..

A comparison among radical approaches to handwritten Chinese character recognition

Robert I. Damper, Steve R. Gunn, Daming Shi, 2001 .

Input Uncertainty in Support Vector Machines

Steve R. Gunn, J. Yang, S. Gunn, 2004 .

Maximum Margin Learning with Incomplete Data: Learning Networks instead of Tables

Steve R. Gunn, Sándor Szedmák, Yizhao Ni, 2010, WAPA.

A Variance Controlled Stochastic Method with Biased Estimation for Faster Non-convex Optimization

Steve R. Gunn, Jia Bi, S. Gunn, 2021, ECML/PKDD.

A Dual Active Contour for Improved Snake Performance

Steve R. Gunn, Nixon, 1995 .

Decision feedback equaliser design using support vector machines

Chris J. Harris, Steve R. Gunn, Sheng Chen, 2000 .

Radical recognition of handwritten Chinese characters using GA-based kernel active shape modelling

Daming Shi, Robert I. Damper, Steve R. Gunn, 2005 .

Predicting relevance of parts of an image

Kitsuchart Pasupa, Gabriela Csurka, Samuel Kaski, 2009 .

Invited review: Adaptive numerical modelling and hybrid physically based ANM approaches in materials engineering – a survey

Steve R. Gunn, Philippa A.S. Reed, Ian Sinclair, 2009 .

Positron Emission Tomography Compartmental Models : A Basis Pursuit Strategy for Kinetic Modelling

Steve R. Gunn, Federico E. Turkheimer, John A. D. Aston, 2002 .

Dual active contour models for image feature extraction

Steve R. Gunn, 1996 .

Tracer kinetic modeling via basis pursuit

Steve R. Gunn, Federico E. Turkheimer, John A. D. Aston, 2002 .

Empirical data modelling algorithms : Additive spline models and support vector machines

Steve R. Gunn, Martin Brown, 1998 .

Active Contours for Head Boundary Extraction by Global and Local Energy Minimisation

Steve R. Gunn, Mark S. Nixon, 1997 .