Martin Brown

发表

Non-Linear Feature Selection for Classification

Nicholas Costen, Martin Brown, Martin Brown, 2004, BMVC.

Dynamic basis pursuit regularization for complex biochemical pathway identification

Martin Brown, Fei He, George Papadopoulos, 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

Multi-Objective Optimisation for Process Design and Control

Martin Brown, Nicky Hutauruk, 2007 .

Construction and Design of Parsimonious Neurofuzzy Systems

Martin Brown, D. J. Mills, Chris J. Harris, 1995 .

Directed Multi-Objective Optimization

Martin Brown, Robert E. Smith, R. Smith, 2005, Int. J. Comput. Syst. Signals.

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).

A novel approach to online pH control on workington BM1

Hong Wang, Martin Brown, Matthew Taylor, 2005 .

The dataflow visualization pipeline as a problem solving environment

Martin Brown, Ken Brodlie, Helen Wright, 1996 .

A Unified Framework of Associative Memory Neural Networks for Adaptive Dynamic Modelling, Control and Estimation

Martin Brown, Chris J. Harris, P. E. An, 1993 .

Aspects of instantaneous on-line learning rules

Martin Brown, Chris J. Harris, P. E. An, 1994 .

Phenotypic Profiling of High Throughput Imaging Screens with Generic Deep Convolutional Features

Martin Brown, Yinhai Wang, Philip T. G. Jackson, 2019, 2019 16th International Conference on Machine Vision Applications (MVA).

Intelligent Neurofuzzy Estimators and Multisensor Data Fusion

Martin Brown, Chris J. Harris, K. M. Bossley, 1997 .

Robust measurement selection for biochemical pathway experimental design

Martin Brown, Fei He, Lam Fat Yeung, 2008, Int. J. Bioinform. Res. Appl..

Support vector machines for optimal classification and spectral unmixing

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

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

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

The Theory and Implementation of the B-spline Neurofuzzy Construction Algorithms

Martin Brown, Chris J. Harris, K. M. Bossley, 1996 .

Delaunay-based nonlinear internal model control

Martin Brown, Thorsten Ullrich, T. Ullrich, 1997 .

Thermal Energy Reduction in Papermaking Industries

Hong Wang, Martin Brown, T. Breikin, 2010 .

ADAM Neural Networks for Parallel Vision

Martin Brown, Jim Austin, Stephen Buckle, 1993 .

Neurofuzzy adaptive modelling and control

Martin Brown, Chris J. Harris, C. Harris, 1994 .

Neurofuzzy Systems Modelling: A Transparent Approach

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

On the convergence rate performance of the normalized least-mean-square adaptation

Martin Brown, Christopher J. Harris, Pak Cheung Edgar An, 1997, IEEE Trans. Neural Networks.

Information-theoretic sensitivity analysis: a general method for credit assignment in complex networks

Martin Brown, Stefano Panzeri, Niklas Lüdtke, 2007, Journal of The Royal Society Interface.

Backward Elimination Methods for Associative Memory Network Pruning

Sheng Chen, Martin Brown, Xia Hong, 2004, Int. J. Hybrid Intell. Syst..

Joint State and Parameter Estimation For Biochemical Dynamic Pathways With Iterative Extended Kalman Filter: Comparison With Dual State and Parameter Estimation

Martin Brown, Zhong Ji, Martin Brown, 2009 .

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.

Neurofuzzy high-dimensional modelling

Martin Brown, D. J. Mills, Chris J. Harris, 1995 .

Theoretical aspects of the CMAC and its application to high-dimensional aerospace modelling problems

Martin Brown, Chris J. Harris, P. E. An, 1994 .

Effective Use of Directional Information in Multi-objective Evolutionary Computation

Martin Brown, Robert E. Smith, 2003, GECCO.

Advances in neurofuzzy algorithms for real-time modelling and control

Martin Brown, D. J. Mills, Chris J. Harris, 1996 .

An Analysis of the Application of B-spline Neurofuzzy Construction Algorithms

Martin Brown, Chris J. Harris, K. M. Bossley, 1996 .

Intelsat VI transmission design and computer system models for FDMA services

Martin Brown, William A. Sandrin, Lan N. Nguyen, 1990 .

Improved Energy Efficiency in Paper Making Through Reducing Dryer Steam Consumption Using Advanced Process Control

Martin Brown, Jan M. Maciejowski, Puya Afshar, 2011 .

FLIERS project: classification software framework

Martin Brown, Hugh G. Lewis, H. Lewis, 1997 .

Simultaneous, Multiplicative Actuator and Sensor Fault Estimation using Fuzzy Observers

Martin Brown, David Blake, Martin Brown, 2007, 2007 IEEE International Fuzzy Systems Conference.

Insights into the behaviour of systems biology models from dynamic sensitivity and identifiability analysis: a case study of an NF-kappaB signalling pathway.

Hong Wang, Martin Brown, Joshua D. Knowles, 2006, Molecular bioSystems.

Generating Test Data for Specification-Based Tests Via Quasirandom Sequences

Martin Brown, Hongmei Chi, Edward L. Jones, 2006, International Conference on Computational Science.

Feature Sensitivity on Biochemical Signaling Pathways

Martin Brown, George Papadopoulos, G. Papadopoulos, 2007, 2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology.

Discrete-Time Model Representations for Biochemical Pathway Modeling

Martin Brown, Fei He, Lam Fat Yeung, 2007, IMECS.

Advances in Neurofuzzy Algorithms for Real Time Modelling, Control and Estimation

Martin Brown, Chris J. Harris, C. Harris, 1994 .

An analysis of value function learning with piecewise linear control

Martin Brown, Onder Tutsoy, Martin Brown, 2016, J. Exp. Theor. Artif. Intell..

On the Analysis of Parameter Convergence for Temporal Difference Learning of an Exemplar Balance Problem

Martin Brown, Onder Tutsoy, 2011, TAROS.

Minimum entropy parameter estimation: Application to the RKIP regulated ERK signaling pathway

Martin Brown, George Papadopoulos, G. Papadopoulos, 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

Support Vector Machines and Linear Spectral Unmixing for Remote Sensing

Martin Brown, Hugh G. Lewis, 1999 .

Neurofuzzy Adaptive Modelling and Construction of Nonlinear Dynamical Processes

Martin Brown, Chris J. Harris, K. M. Bossley, 1995 .

A global gradient-noise covariance expression for stationary real Gaussian inputs

Martin Brown, Christopher J. Harris, Pak Cheung Edgar An, 1995, IEEE Trans. Neural Networks.

Evaluation of Tangible User Interfaces (TUIs) for and with Children - Methods and Challenges

Janet C. Read, Martin Brown, Emanuela Mazzone, 2007, HCI.

Using the Torso to Compensate for Non-Minimum Phase Behaviour in ZMP Bipedal Walking

Martin Brown, Bram Vanderborght, Houman Dallali, 2009 .

On the Convergence of Multi-Objective Descent Algorithms

Martin Brown, Nicky Hutauruk, Martin Brown, 2007, 2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making.

FLIERS software framework and toolkit version 2.0: user guides

Martin Brown, Hugh G. Lewis, 1998 .

A Comparison of Multivariable & Decentralized Control Strategies for Robust Humanoid Walking

G. A. Medrano-Cerda, Martin Brown, Houman Dallali, 2010 .

Data-Based Robust Multiobjective Optimization of Interconnected Processes: Energy Efficiency Case Study in Papermaking

Martin Brown, Jan M. Maciejowski, Hong Wang, 2011, IEEE Transactions on Neural Networks.

Neurofuzzy Algorithms for Model Identification: Structure and Parameter Determination

Martin Brown, Chris J. Harris, K. M. Bossley, 1996 .

Intelligent data modelling using neurofuzzy algorithms

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

ADVANCES IN NEUROFUZZY ALGORITHMS FOR REAL TIME MODELLING, CONTROL AND ESTIMATION

Martin Brown, Chris J. Harris, 1995 .

Middleware polishes c/s

Martin Brown, Martin C. Brown, 1993 .

Intelligent Control - Aspects of Fuzzy Logic and Neural Nets

Martin Brown, Christopher J. Harris, Chris G. Moore, 1993, World Scientific Series in Robotics and Intelligent Systems.

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).

Perl programmer's reference

Martin Brown, 1999 .

Exploratory Sparse Models for Face Recognition

Nicholas Costen, Martin Brown, 2003, BMVC.

Fusion of soft information using TBM

Martin Brown, Gavin Powell, Dafni Stampouli, 2010, 2010 13th International Conference on Information Fusion.

Exploratory basis pursuit classification

Nicholas Costen, Martin Brown, Martin Brown, 2005, Pattern Recognit. Lett..

On Reduced Structure Control and the 1-norm regularisation

Martin Brown, Amin Nobakhti, Hong Wang, 2007, 2007 IEEE International Conference on Networking, Sensing and Control.

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..

High-dimensional approximation using an associative memory network

Martin Brown, D. J. Mills, Chris J. Harris, 1994 .

Decentralized LQR Joint Servo Design for a Compliant Humanoid Robot via LMI Optimisation

Nikos G. Tsagarakis, Darwin G. Caldwell, Martin Brown, 2012 .

On the complexity - sensitivity trade-off for the NF-κB pathway modeling

Martin Brown, Fei He, Lam Fat Yeung, 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

Comparative Aspects of Associative Memory Networks for Modelling

Martin Brown, Chris J. Harris, P. E. An, 1993 .

Efficient calculation of the complete optimal classification set

Nicholas Costen, Martin Brown, Shigeru Akamatsu, 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

Neurofuzzy Model Construction for the Modelling of Non-linear Processes

Martin Brown, Chris J. Harris, K. M. Bossley, 1995 .

High Order Instantaneous Learning Algorithms for On-line Training

Martin Brown, D. J. Mills, Chris J. Harris, 1994 .

Designing and testing a tangible interface prototype

Janet C. Read, Martin Brown, Emanuela Mazzone, 2007, IDC.

Robust experimental design and feature selection in signal transduction pathway modeling

Martin Brown, Fei He, Lam Fat Yeung, 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

Improving Project Management of Complex or Major Systems

Martin Brown, Charles Pickar, William E Taylor, 2016 .

Comments on "Learning convergence in the cerebellar model articulation controller"

Martin Brown, Christopher J. Harris, 1995, IEEE Trans. Neural Networks.

On Real Time Vehicle Guidance and Driver Modelling Within Prometheus

Martin Brown, Chris J. Harris, P. E. An, 1994 .

High dimensional neurofuzzy systems: overcoming the curse of dimensionality

Martin Brown, D. J. Mills, Chris J. Harris, 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

On the sensitivity of the control structure selection problems in large scale multivariable systems

Martin Brown, Amin Nobakhti, Hong Wang, 2007, 2007 46th IEEE Conference on Decision and Control.

Comparative study of stabilising methods for adaptive speed sensorless full-order observers with stator resistance estimation

Martin Brown, I. Vicente, A. Endemano, 2010 .

The Threat of Exclusion and Implicit Contracting

Martin Brown, Marta Serra-Garcia, Marta Serra-Garcia, 2016, Manag. Sci..

Investigating Bayesian robust experimental design with principles of global sensitivity analysis

Martin Brown, Fei He, Hong Yue, 2010 .

Comparative Aspects of Neural Network Algorithms for On-Line Modelling of Dynamic Processes

Martin Brown, Chris J. Harris, Sheng Chen, 1993 .

Stable MRAS-based sensorless scheme design strategy for high power traction drives

Martin Brown, Alasdair Renfrew, I. Vicente, 2008 .

A Probabilistic Framework for SVM Regression and Error Bar Estimation

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

Associative Memory Network Construction Algorithms

Martin Brown, D. J. Mills, Chris J. Harris, 1994 .

Sparse models for gender classification

Nicholas Costen, Martin Brown, Shigeru Akamatsu, 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

Fault detection for a class of nonlinear systems via associative memory neural networks

Martin Brown, Hong Wang, C. J. Harris, 1994 .

Understanding Bank-Run Contagion

Martin Brown, Stefan T. Trautmann, Razvan Vlahu, 2014, Manag. Sci..

A perspective and critique of adaptive neurofuzzy systems used for modelling and control applications

Martin Brown, Christopher J. Harris, 1995, Int. J. Neural Syst..