Gavin Brown

发表

Online, GA based mixture of experts: a probabilistic model of ucs

Narayanan Unny Edakunni, Gavin Brown, Tim Kovacs, 2011, GECCO '11.

Bayesian estimation of rule accuracy in UCS

Gavin Brown, Tim Kovacs, James A. R. Marshall, 2007, GECCO '07.

Modular Autoencoders for Ensemble Feature Extraction pdf

Gavin Brown, Henry W. J. Reeve, 2015, FE@NIPS.

General Terminology Induction in OWL

Gavin Brown, Ulrike Sattler, Viachaslau Sazonau, 2015, International Semantic Web Conference.

General Terminology Induction in OWL

Gavin Brown, Ulrike Sattler, Viachaslau Sazonau, 2015, OWLED.

ManTIME: Temporal expression identification and normalization in the TempEval-3 challenge pdf

Goran Nenadic, Gavin Brown, Michele Filannino, 2013, SemEval@NAACL-HLT.

Markov Blanket Discovery in Positive-Unlabelled and Semi-supervised Data

Gavin Brown, Konstantinos Sechidis, Konstantinos Sechidis, 2015, ECML/PKDD.

Negative Correlation Learning and the Ambiguity Family of Ensemble Methods

Gavin Brown, Jeremy L. Wyatt, 2003, Multiple Classifier Systems.

WHOAMI: A Method For Identifying a Domain Operator's Contact Information

Gavin Brown, 2018 .

Distinguishing prognostic and predictive biomarkers: an information theoretic approach

Gavin Brown, James Weatherall, Konstantinos Sechidis, 2018, Bioinform..

Boosting Java Performance Using GPGPUs pdf

Gavin Brown, Mikel Luján, Christos Kotselidis, 2015, ARCS.

Theoretical and empirical analysis of diversity in non-stationary learning

Gavin Brown, Richard John Stapenhurst, 2011, 2011 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE).

Managing Diversity in Regression Ensembles

Peter Tiño, Gavin Brown, Jeremy L. Wyatt, 2005, J. Mach. Learn. Res..

Information Theoretic Feature Selection in Multi-label Data through Composite Likelihood

Gavin Brown, Konstantinos Sechidis, Nikolaos Nikolaou, 2014, S+SSPR.

Measuring the Stability of Feature Selection with Applications to Ensemble Methods

Gavin Brown, Sarah Nogueira, Gavin Brown, 2015, MCS.

Fundamental Nano-Patterns to Characterize and Classify Java Methods

Gavin Brown, Jeremy Singer, Mikel Luján, 2010, LDTA.

Efficient feature selection using shrinkage estimators

Gavin Brown, Adam Pocock, Laura Azzimonti, 2019, Machine Learning.

Toward an Understanding of Adversarial Examples in Clinical Trials

Gavin Brown, James Weatherall, Konstantinos Sechidis, 2018, ECML/PKDD.

On the Stability of Feature Selection Algorithms

Gavin Brown, Konstantinos Sechidis, Sarah Nogueira, 2017, J. Mach. Learn. Res..

Ensemble Learning in Linearly Combined Classifiers Via Negative Correlation

Gavin Brown, Fabio Roli, Giorgio Fumera, 2007, MCS.

Cost-sensitive boosting algorithms: Do we really need them?

Peter A. Flach, Narayanan Unny Edakunni, Gavin Brown, 2016, Machine Learning.

Diversity creation methods: a survey and categorisation

Xin Yao, Gavin Brown, Jeremy L. Wyatt, 2004, Inf. Fusion.

Random Ordinality Ensembles A Novel Ensemble Method for Multi-valued Categorical Data

Gavin Brown, Amir Ahmad, Gavin Brown, 2009, MCS.

The economics of garbage collection

Gavin Brown, Jeremy Singer, Richard E. Jones, 2010, ISMM '10.

Accuracy exponentiation in UCS and its effect on voting margins

Narayanan Unny Edakunni, Gavin Brown, Tim Kovacs, 2011, GECCO '11.

Modeling UCS as a mixture of experts

Narayanan Unny Edakunni, Gavin Brown, Tim Kovacs, 2009, GECCO '09.

Statistical Hypothesis Testing in Positive Unlabelled Data

Gavin Brown, Borja Calvo, Konstantinos Sechidis, 2014, ECML/PKDD.

Modular Dimensionality Reduction

Tingting Mu, Gavin Brown, Henry W. J. Reeve, 2018, ECML/PKDD.

The Use of the Ambiguity Decomposition in Neural Network Ensemble Learning Methods

Gavin Brown, Jeremy L. Wyatt, J. Wyatt, 2003, ICML.

Compiler-Driven Software Speculation for Thread-Level Parallelism

Gavin Brown, Mikel Luján, Paraskevas Yiapanis, 2015, ACM Trans. Program. Lang. Syst..

Learn++.MF: A random subspace approach for the missing feature problem

Gavin Brown, Robi Polikar, Ludmila I. Kuncheva, 2010, Pattern Recognit..

Modelling 3D Topographic Space Against Indoor Navigation Requirements

Gavin Brown, Sisi Zlatanova, Thomas H. Kolbe, 2013 .

Random Ordinality Ensembles: Ensemble methods for multi-valued categorical data

Gavin Brown, Amir Ahmad, Gavin Brown, 2015, Inf. Sci..

Predicting OWL Reasoners: Locally or Globally?

Gavin Brown, Ulrike Sattler, Viachaslau Sazonau, 2014, Description Logics.

Command Reversal Extension for the Extensible Provisioning Protocol (EPP)

Gavin Brown, Jothan Frakes, 2015 .

A New Perspective for Information Theoretic Feature Selection

Gavin Brown, Gavin Brown, 2009, AISTATS.

Diversity and degrees of freedom in regression ensembles

Gavin Brown, Henry W. J. Reeve, Gavin Brown, 2018, Neurocomputing.

Ranking Biomarkers Through Mutual Information pdf

Gavin Brown, James Weatherall, Konstantinos Sechidis, 2016, ArXiv.

Ensemble Learning

Gavin Brown, Gavin Brown, Gavin Brown, 2010, Encyclopedia of Machine Learning and Data Mining.

The K-Nearest Neighbour UCB Algorithm for Multi-Armed Bandits with Covariates pdf

Gavin Brown, Henry W. J. Reeve, Joseph C. Mellor, 2018, ALT.

Insights into distributed feature ranking

Verónica Bolón-Canedo, Amparo Alonso-Betanzos, Gavin Brown, 2019, Inf. Sci..

UCSpv: principled voting in UCS rule populations

Gavin Brown, Tim Kovacs, James A. R. Marshall, 2007, GECCO '07.

On the Use of Spearman's Rho to Measure the Stability of Feature Rankings

Gavin Brown, Konstantinos Sechidis, Sarah Nogueira, 2017, IbPRIA.

Online Non-stationary Boosting

Gavin Brown, Jeremy Singer, Mikel Luján, 2010, MCS.

ORB-SLAM-CNN: Lessons in Adding Semantic Map Construction to Feature-Based SLAM

Gavin Brown, Andrew M. Webb, Mikel Luján, 2019, TAROS.

Is Deep Learning Safe for Robot Vision? Adversarial Examples Against the iCub Humanoid

Gavin Brown, Fabio Roli, Marco Melis, 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).

Towards intelligent analysis techniques for object pretenuring

Gavin Brown, Jeremy Singer, Mikel Luján, 2007, PPPJ.

Estimating Mutual Information in Under-Reported Variables

Gavin Brown, Matthew Sperrin, Konstantinos Sechidis, 2016, Probabilistic Graphical Models.

A scalable implementation of information theoretic feature selection for high dimensional data

Gavin Brown, Anthony Kleerekoper, Mikel Luján, 2015, 2015 IEEE International Conference on Big Data (Big Data).

Return Value Prediction meets Information Theory

Gavin Brown, Jeremy Singer, Jeremy Singer, 2006, QAPL.

Measuring the Stability of Feature Selection

Gavin Brown, Sarah Nogueira, Gavin Brown, 2016, ECML/PKDD.

Toward a more accurate understanding of the limits of the TLS execution paradigm

Gavin Brown, Jeremy Singer, Nikolas Ioannou, 2010, IEEE International Symposium on Workload Characterization (IISWC'10).

Intelligent selection of application-specific garbage collectors

Gavin Brown, Jeremy Singer, John Cavazos, 2007, ISMM '07.

Gradient Boosting Models for Photovoltaic Power Estimation Under Partial Shading Conditions

Gavin Brown, Efstratios I. Batzelis, Nikolaos Nikolaou, 2017, DARE@PKDD/ECML.

Diversity in neural network ensembles

Gavin Brown, Gavin Brown, 2004 .

Minimax rates for cost-sensitive learning on manifolds with approximate nearest neighbours

Gavin Brown, Henry W. J. Reeve, Gavin Brown, 2017, ALT.

Some Thoughts at the Interface of Ensemble Methods and Feature Selection

Gavin Brown, 2010, MCS.

Informative Priors for Markov Blanket Discovery

Gavin Brown, Mikel Luján, Adam Craig Pocock, 2012, AISTATS.

Degrees of Freedom in Regression Ensembles

Gavin Brown, Henry W. J. Reeve, 2017, ESANN.

"Good" and "Bad" Diversity in Majority Vote Ensembles

Gavin Brown, Ludmila I. Kuncheva, L. Kuncheva, 2010, MCS.

Exploring sketches for probability estimation with sublinear memory

Gavin Brown, Anthony Kleerekoper, Mikel Luján, 2013, 2013 IEEE International Conference on Big Data.

Random Projection Random Discretization Ensembles—Ensembles of Linear Multivariate Decision Trees

Gavin Brown, Amir Ahmad, Gavin Brown, 2014, IEEE Transactions on Knowledge and Data Engineering.

Calibrating AdaBoost for Asymmetric Learning

Gavin Brown, Nikolaos Nikolaou, N. Nikolaou, 2015, MCS.

Distinguishing prognostic and predictive biomarkers: an information theoretic approach

Gavin Brown, James Weatherall, Konstantinos Sechidis, 2018, Bioinform..

Exploiting ensemble diversity for automatic feature extraction

Heiko Wersing, Bernhard Sendhoff, Gavin Brown, 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

Deep learning-based classification and anomaly detection of side-channel signals

Gavin Brown, Quan Zhou, John Wang, 2018, Defense + Security.

Predicting Performance of OWL Reasoners: Locally or Globally?

Gavin Brown, Ulrike Sattler, Viachaslau Sazonau, 2014, KR.

Analytic Solutions to Differential Equations under Graph-Based Genetic Programming

Gavin Brown, Julian Francis Miller, Tom Seaton, 2010, EuroGP.

Correction to: Efficient feature selection using shrinkage estimators

Gavin Brown, Adam Pocock, Laura Azzimonti, 2020, Machine Learning.

Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection

Gavin Brown, Mikel Luján, Adam Craig Pocock, 2012, J. Mach. Learn. Res..

Joint Training of Neural Network Ensembles pdf

Gavin Brown, Andrew M. Webb, Mikel Luján, 2019, ArXiv.

A Study of Random Linear Oracle Ensembles

Gavin Brown, Amir Ahmad, 2009, MCS.

Beyond Fano's inequality: bounds on the optimal F-score, BER, and cost-sensitive risk and their implications

Narayanan Unny Edakunni, Gavin Brown, Adam Craig Pocock, 2013, J. Mach. Learn. Res..

Garbage collection auto-tuning for Java mapreduce on multi-cores

Gavin Brown, Jeremy Singer, Mikel Luján, 2011, ISMM '11.

Between Two Extremes: Examining Decompositions of the Ensemble Objective Function

Gavin Brown, Ping Sun, Jeremy L. Wyatt, 2005, Multiple Classifier Systems.

A Study of Semi-supervised Generative Ensembles

Gavin Brown, Manuela Zanda, Gavin Brown, 2009, MCS.

An Information Theoretic Perspective on Multiple Classifier Systems

Gavin Brown, 2009, MCS.

Dealing with under-reported variables: An information theoretic solution

Gavin Brown, Mikel Luján, Matthew Sperrin, 2017, Int. J. Approx. Reason..

Is Feature Selection Secure against Training Data Poisoning?

Claudia Eckert, Gavin Brown, Fabio Roli, 2015, ICML.

Conditional Likelihood Maximisation: A Unifying Framework for Mutual Information Feature Selection

Gavin Brown, Mikel Luján, Adam Craig Pocock, 2012 .

Branch Prediction with Bayesian Networks

Gavin Brown, Jeremy Singer, Ian Watson, 2007 .