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Peter Flach
Peter Flach
Professor of Artificial Intelligence, Department of Computer Science, University of Bristol
Dirección de correo verificada de bristol.ac.uk - Página principal
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Machine learning: the art and science of algorithms that make sense of data
P Flach
Cambridge university press, 2012
20172012
On graph kernels: Hardness results and efficient alternatives
T Gärtner, P Flach, S Wrobel
Learning Theory and Kernel Machines: 16th Annual Conference on Learning …, 2003
13052003
Multi-instance kernels
T Gärtner, PA Flach, A Kowalczyk, AJ Smola
ICML 2 (3), 7, 2002
7332002
CRISP-DM twenty years later: From data mining processes to data science trajectories
F Martínez-Plumed, L Contreras-Ochando, C Ferri, J Hernández-Orallo, ...
IEEE transactions on knowledge and data engineering 33 (8), 3048-3061, 2019
6072019
Rule evaluation measures: A unifying view
N Lavrač, P Flach, B Zupan
International Conference on Inductive Logic Programming, 174-185, 1999
6051999
Subgroup discovery with CN2-SD
N Lavrač, B Kavšek, P Flach, L Todorovski
Journal of Machine Learning Research 5 (Feb), 153-188, 2004
5722004
Precision-recall-gain curves: PR analysis done right
P Flach, M Kull
Advances in neural information processing systems 28, 2015
4972015
The geometry of ROC space: understanding machine learning metrics through ROC isometrics
PA Flach
Proceedings of the 20th international conference on machine learning (ICML …, 2003
4412003
Learning decision trees using the area under the ROC curve
C Ferri, P Flach, J Hernández-Orallo
Icml 2, 139-146, 2002
4412002
FACE: feasible and actionable counterfactual explanations
R Poyiadzi, K Sokol, R Santos-Rodriguez, T De Bie, P Flach
Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 344-350, 2020
4352020
Propositionalization approaches to relational data mining
S Kramer, N Lavrač, P Flach
Relational data mining, 262-291, 2001
4182001
Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with dirichlet calibration
M Kull, M Perello Nieto, M Kängsepp, T Silva Filho, H Song, P Flach
Advances in neural information processing systems 32, 2019
4122019
Explainability fact sheets: A framework for systematic assessment of explainable approaches
K Sokol, P Flach
Proceedings of the 2020 conference on fairness, accountability, and …, 2020
4082020
A coherent interpretation of AUC as a measure of aggregated classification performance
PA Flach, J Hernández-Orallo, C Ferri
Proceedings of the 28th International Conference on Machine Learning (ICML …, 2011
3622011
Bridging e-health and the internet of things: The sphere project
N Zhu, T Diethe, M Camplani, L Tao, A Burrows, N Twomey, D Kaleshi, ...
IEEE Intelligent Systems 30 (4), 39-46, 2015
3362015
Roc ‘n’rule learning—towards a better understanding of covering algorithms
J Fürnkranz, PA Flach
Machine learning 58, 39-77, 2005
3282005
Abduction and Induction: Essays on their relation and integration
PA Flach, AC Kakas
Kluwer Academic Publishers, 2000
2962000
A unified view of performance metrics: Translating threshold choice into expected classification loss
J Hernández-Orallo, P Flach, C Ferri
The Journal of Machine Learning Research 13 (1), 2813-2869, 2012
2852012
Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers
M Kull, T Silva Filho, P Flach
Artificial intelligence and statistics, 623-631, 2017
2452017
Database dependency discovery: a machine learning approach
PA Flach, I Savnik
AI communications 12 (3), 139-160, 1999
2411999
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Artículos 1–20