Peter Flach

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Meelis KullAssociate Professor, University of TartuVerified email at ut.ee
Tom DietheAmazon; University of BristolVerified email at bristol.ac.uk
José Hernández-OralloVRAIN, Valencian Research Institute for Artificial Intelligence, Universitat Politecnica de ValenciaVerified email at upv.es
César FerriVRAIN. Universitat Politècnica de ValènciaVerified email at dsic.upv.es
Niall TwomeyPrincipal Applied Scientist at KidsLoop Ltd; Honorary Research Fellow, University of Bristol, UKVerified email at bristol.ac.uk
Nicolas LachicheAssociate professor in Computer Science, University of StrasbourgVerified email at unistra.fr
Thomas GärtnerTU Wien (Technical University of Vienna)Verified email at tuwien.ac.at
Kacper SokolRMIT UniversityVerified email at rmit.edu.au
Hao SongSeldon; University of BristolVerified email at bristol.ac.uk
Raúl Santos-RodríguezUniversity of BristolVerified email at bristol.ac.uk
Ian CraddockUniversity of BristolVerified email at bristol.ac.uk
Antonis KakasUniversity of CyprusVerified email at ucy.ac.cy
Miquel Perelló-NietoPhD Candidate at University of BristolVerified email at bristol.ac.uk
Simon PriceResearch Fellow, University of BristolVerified email at bristol.ac.uk
Shaomin WuProfessor of Business/Applied Statistics, Kent Business School, University of KentVerified email at kent.ac.uk
Stefan WrobelFraunhofer IAIS and University of BonnVerified email at iais.fraunhofer.de
Tijl De BieFull Professor, Ghent UniversityVerified email at ugent.be
Johannes FürnkranzJohannes Kepler University, LinzVerified email at faw.jku.at
Louise Amanda Claire MillardUniversity of BristolVerified email at bristol.ac.uk
Simon RawlesUniversity of NottinghamVerified email at rawles.net

Peter Flach

Professor of Artificial Intelligence, Department of Computer Science, University of Bristol

Verified email at bristol.ac.uk - Homepage

Artificial Intelligence Data Science Machine Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Machine learning: the art and science of algorithms that make sense of data P Flach Cambridge university press, 2012	1402	2012
On graph kernels: Hardness results and efficient alternatives T Gärtner, P Flach, S Wrobel Learning theory and kernel machines, 129-143, 2003	1093	2003
Multi-instance kernels T Gärtner, PA Flach, A Kowalczyk, AJ Smola ICML 2 (3), 7, 2002	650	2002
Rule evaluation measures: A unifying view N Lavrač, P Flach, B Zupan International Conference on Inductive Logic Programming, 174-185, 1999	568	1999
Subgroup discovery with CN2-SD N Lavrač, B Kavšek, P Flach, L Todorovski Journal of Machine Learning Research 5 (Feb), 153-188, 2004	514	2004
Learning decision trees using the area under the ROC curve C Ferri, P Flach, J Hernández-Orallo Icml 2, 139-146, 2002	412	2002
Propositionalization approaches to relational data mining S Kramer, N Lavrač, P Flach Relational data mining, 262-291, 2001	403	2001
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	373	2003
Roc ‘n’rule learning—towards a better understanding of covering algorithms J Fürnkranz, PA Flach Machine learning 58 (1), 39-77, 2005	304	2005
Abduction and Induction: Essays on their relation and integration PA Flach, AC Kakas Kluwer Academic Publishers, 2000	261*	2000
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	260	2015
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	252	2011
Precision-recall-gain curves: PR analysis done right P Flach, M Kull Advances in neural information processing systems 28, 2015	242	2015
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	220	2012
Kernels and distances for structured data T Gärtner, JW Lloyd, PA Flach Machine Learning 57 (3), 205-232, 2004	208	2004
Database dependency discovery: a machine learning approach PA Flach, I Savnik AI communications 12 (3), 139-160, 1999	207	1999
Confirmation-guided discovery of first-order rules with Tertius PA Flach, N Lachiche Machine learning 42 (1), 61-95, 2001	206	2001
Naive Bayesian classification of structured data PA Flach, N Lachiche Machine learning 57 (3), 233-269, 2004	191	2004
Simply logical: intelligent reasoning by example P Flach John Wiley & Sons, Inc., 1994	187	1994
Comparative evaluation of approaches to propositionalization MA Krogel, S Rawles, F Železný, PA Flach, N Lavrač, S Wrobel International Conference on Inductive Logic Programming, 197-214, 2003	182	2003

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