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Peter Flach
Peter Flach
Professor of Artificial Intelligence, Department of Computer Science, University of Bristol
Adresse e-mail validée de bristol.ac.uk - Page d'accueil
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Machine learning: the art and science of algorithms that make sense of data
P Flach
Cambridge university press, 2012
17502012
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
12212003
Multi-instance kernels
T Gärtner, PA Flach, A Kowalczyk, AJ Smola
ICML 2 (3), 7, 2002
7002002
Rule evaluation measures: A unifying view
N Lavrač, P Flach, B Zupan
International Conference on Inductive Logic Programming, 174-185, 1999
5901999
Subgroup discovery with CN2-SD
N Lavrač, B Kavšek, P Flach, L Todorovski
Journal of Machine Learning Research 5 (Feb), 153-188, 2004
5452004
Learning decision trees using the area under the ROC curve
C Ferri, P Flach, J Hernández-Orallo
Icml 2, 139-146, 2002
4332002
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
4172003
Propositionalization approaches to relational data mining
S Kramer, N Lavrač, P Flach
Relational data mining, 262-291, 2001
4142001
Precision-recall-gain curves: PR analysis done right
P Flach, M Kull
Advances in neural information processing systems 28, 2015
3632015
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
3462019
Roc ‘n’rule learning—towards a better understanding of covering algorithms
J Fürnkranz, PA Flach
Machine learning 58, 39-77, 2005
3142005
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
3062015
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
3032011
Abduction and Induction: Essays on their relation and integration
PA Flach, AC Kakas
Kluwer Academic Publishers, 2000
279*2000
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
2742020
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
2722020
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
2662019
A unified view of performance metrics: Translating threshold choice into expected classification loss
J Hernández-Orallo, P Flach, C Ferri Ramírez
Journal of Machine Learning Research 13, 2813-2869, 2012
2452012
Database dependency discovery: a machine learning approach
PA Flach, I Savnik
AI communications 12 (3), 139-160, 1999
2321999
Confirmation-guided discovery of first-order rules with Tertius
PA Flach, N Lachiche
Machine learning 42, 61-95, 2001
2202001
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