| Estimating time-varying brain connectivity networks from functional MRI time series RP Monti, P Hellyer, D Sharp, R Leech, C Anagnostopoulos, G Montana NeuroImage 103, 427-443, 2014 | 192 | 2014 |
| When is the area under the receiver operating characteristic curve an appropriate measure of classifier performance? DJ Hand, C Anagnostopoulos Pattern Recognition Letters 34 (5), 492-495, 2013 | 106 | 2013 |
| The Automatic Neuroscientist: A framework for optimizing experimental design with closed-loop real-time fMRI R Lorenz, RP Monti, IR Violante, C Anagnostopoulos, AA Faisal, ... NeuroImage 129, 320-334, 2016 | 91 | 2016 |
| A better Beta for the H measure of classification performance DJ Hand, C Anagnostopoulos Pattern Recognition Letters 40, 41-46, 2014 | 80 | 2014 |
| Online linear and quadratic discriminant analysis with adaptive forgetting for streaming classification C Anagnostopoulos, DK Tasoulis, NM Adams, NG Pavlidis, DJ Hand Statistical Analysis and Data Mining: The ASA Data Science Journal 5 (2 …, 2012 | 71 | 2012 |
| Financial time series modeling using the Hurst exponent S Tzouras, C Anagnostopoulos, E McCoy Physica A: Statistical Mechanics and its Applications 425, 50-68, 2015 | 53 | 2015 |
| Stopping criteria for boosting automatic experimental design using real-time fMRI with Bayesian optimization R Lorenz, RP Monti, IR Violante, AA Faisal, C Anagnostopoulos, R Leech, ... arXiv preprint arXiv:1511.07827, 2015 | 38 | 2015 |
| Temporally-adaptive linear classification for handling population drift in credit scoring NM Adams, DK Tasoulis, C Anagnostopoulos, DJ Hand Proceedings of COMPSTAT'2010: 19th International Conference on Computational …, 2010 | 32 | 2010 |
| Real‐time estimation of dynamic functional connectivity networks RP Monti, R Lorenz, RM Braga, C Anagnostopoulos, R Leech, G Montana Human brain mapping 38 (1), 202-220, 2017 | 29 | 2017 |
| Learning population and subject-specific brain connectivity networks via mixed neighborhood selection RP Monti, C Anagnostopoulos, G Montana The Annals of Applied Statistics, 2142-2164, 2017 | 20 | 2017 |
| Temporally adaptive estimation of logistic classifiers on data streams C Anagnostopoulos, DK Tasoulis, NM Adams, DJ Hand Advances in data analysis and classification 3, 243-261, 2009 | 20 | 2009 |
| Online optimization for variable selection in data streams C Anagnostopoulos, D Tasoulis, DJ Hand, NM Adams ECAI 2008, 132-136, 2008 | 19 | 2008 |
| Measuring classification performance: the hmeasure package. C Anagnostopoulos UFIL: http://llcran. r-project. orglweblpackageslhmea-surelvignetteslhmeasurepdf, 2012 | 17 | 2012 |
| Deciding what to observe next: adaptive variable selection for regression in multivariate data streams C Anagnostopoulos, NM Adams, DJ Hand Proceedings of the 2008 ACM symposium on Applied computing, 961-965, 2008 | 17 | 2008 |
| Notes on the H-measure of classifier performance DJ Hand, C Anagnostopoulos Advances in Data Analysis and Classification 17 (1), 109-124, 2023 | 16 | 2023 |
| Package ‘hmeasure’ C Anagnostopoulos, M Suggests, MC Anagnostopoulos | 16 | 2012 |
| Towards tailoring non-invasive brain stimulation using real-time fMRI and Bayesian optimization R Lorenz, RP Monti, A Hampshire, Y Koush, C Anagnostopoulos, ... 2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI), 1-4, 2016 | 15 | 2016 |
| A statistical framework for streaming data analysis C Anagnostopoulos Imperial College London, 2010 | 14 | 2010 |
| Adaptive regularization for lasso models in the context of nonstationary data streams RP Monti, C Anagnostopoulos, G Montana Statistical Analysis and Data Mining: The ASA Data Science Journal 11 (5 …, 2018 | 12 | 2018 |
| When does active learning work? LPG Evans, NM Adams, C Anagnostopoulos Advances in Intelligent Data Analysis XII: 12th International Symposium, IDA …, 2013 | 11 | 2013 |
