| 2009 |
|---|
| 70 | EE | Antonis C. Kakas,
Peter A. Flach:
Abduction and Induction in Artificial Intelligence.
J. Applied Logic 7(3): 251 (2009) |
| 2007 |
|---|
| 69 | EE | Shaomin Wu,
Peter A. Flach,
Cèsar Ferri Ramirez:
An Improved Model Selection Heuristic for AUC.
ECML 2007: 478-489 |
| 68 | EE | Peter A. Flach,
Edson Takashi Matsubara:
A Simple Lexicographic Ranker and Probability Estimator.
ECML 2007: 575-582 |
| 67 | EE | Peter A. Flach:
Putting Things in Order: On the Fundamental Role of Ranking in Classification and Probability Estimation.
ECML/PKDD 2007: 2-3 |
| 66 | EE | Peter A. Flach,
Edson Takashi Matsubara:
On classification, ranking, and probability estimation.
Probabilistic, Logical and Relational Learning - A Further Synthesis 2007 |
| 2006 |
|---|
| 65 | EE | Peter A. Flach:
Reinventing Machine Learning with ROC Analysis.
IBERAMIA-SBIA 2006: 4-5 |
| 64 | EE | Kerstin Eder,
Peter A. Flach,
Hsiou-Wen Hsueh:
Towards Automating Simulation-Based Design Verification Using ILP.
ILP 2006: 154-168 |
| 2005 |
|---|
| 63 | EE | Elias Gyftodimos,
Peter A. Flach:
Combining Bayesian Networks with Higher-Order Data Representations.
IDA 2005: 145-156 |
| 62 | EE | Peter A. Flach,
Shaomin Wu:
Repairing Concavities in ROC Curves.
IJCAI 2005: 702-707 |
| 61 | EE | Ronaldo C. Prati,
Peter A. Flach:
ROCCER: An Algorithm for Rule Learning Based on ROC Analysis.
IJCAI 2005: 823-828 |
| 60 | EE | Elias Gyftodimos,
Peter A. Flach:
Combining Bayesian Networks with Higher-Order Data Representations.
Probabilistic, Logical and Relational Learning 2005 |
| 59 | EE | Tom Fawcett,
Peter A. Flach:
A Response to Webb and Ting's On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions.
Machine Learning 58(1): 33-38 (2005) |
| 58 | EE | Johannes Fürnkranz,
Peter A. Flach:
ROC 'n' Rule Learning-Towards a Better Understanding of Covering Algorithms.
Machine Learning 58(1): 39-77 (2005) |
| 2004 |
|---|
| 57 | | José Hernández-Orallo,
César Ferri,
Nicolas Lachiche,
Peter A. Flach:
ROC Analysis in Artificial Intelligence, 1st International Workshop, ROCAI-2004, Valencia, Spain, August 22, 2004
ROCAI 2004 |
| 56 | EE | Johannes Fürnkranz,
Peter A. Flach:
An Analysis of Stopping and Filtering Criteria for Rule Learning.
ECML 2004: 123-133 |
| 55 | EE | César Ferri,
Peter A. Flach,
José Hernández-Orallo:
Delegating classifiers.
ICML 2004 |
| 54 | EE | Annalisa Appice,
Michelangelo Ceci,
Simon Rawles,
Peter A. Flach:
Redundant feature elimination for multi-class problems.
ICML 2004 |
| 53 | EE | Elias Gyftodimos,
Peter A. Flach:
Hierarchical Bayesian Networks: An Approach to Classification and Learning for Structured Data.
SETN 2004: 291-300 |
| 52 | EE | Nada Lavrac,
Branko Kavsek,
Peter A. Flach,
Ljupco Todorovski:
Subgroup Discovery with CN2-SD.
Journal of Machine Learning Research 5: 153-188 (2004) |
| 51 | EE | Nada Lavrac,
Bojan Cestnik,
Dragan Gamberger,
Peter A. Flach:
Decision Support Through Subgroup Discovery: Three Case Studies and the Lessons Learned.
Machine Learning 57(1-2): 115-143 (2004) |
| 50 | EE | Thomas Gärtner,
John W. Lloyd,
Peter A. Flach:
Kernels and Distances for Structured Data.
Machine Learning 57(3): 205-232 (2004) |
| 49 | EE | Peter A. Flach,
Nicolas Lachiche:
Naive Bayesian Classification of Structured Data.
Machine Learning 57(3): 233-269 (2004) |
| 48 | EE | José Hernández-Orallo,
César Ferri,
Nicolas Lachiche,
Peter A. Flach:
The 1st workshop on ROC analysis in artificial intelligence (ROCAI-2004).
SIGKDD Explorations 6(2): 159-161 (2004) |
| 47 | | Peter A. Flach:
Book review: Logic for Learning: Learning Comprehensible Theories from Structured Data by John W. Lloyd, Springer-Verlag, 2003, ISBN 3-540-42027-4.
TPLP 4(5-6): 753-755 (2004) |
| 2003 |
|---|
| 46 | EE | Thomas Gärtner,
Peter A. Flach,
Stefan Wrobel:
On Graph Kernels: Hardness Results and Efficient Alternatives.
COLT 2003: 129-143 |
| 45 | EE | César Ferri,
Peter A. Flach,
José Hernández-Orallo:
Improving the AUC of Probabilistic Estimation Trees.
ECML 2003: 121-132 |
| 44 | | Peter A. Flach:
The Geometry of ROC Space: Understanding Machine Learning Metrics through ROC Isometrics.
ICML 2003: 194-201 |
| 43 | | Johannes Fürnkranz,
Peter A. Flach:
An Analysis of Rule Evaluation Metrics.
ICML 2003: 202-209 |
| 42 | | Nicolas Lachiche,
Peter A. Flach:
Improving Accuracy and Cost of Two-class and Multi-class Probabilistic Classifiers Using ROC Curves.
ICML 2003: 416-423 |
| 41 | EE | Mark-A. Krogel,
Simon Rawles,
Filip Zelezný,
Peter A. Flach,
Nada Lavrac,
Stefan Wrobel:
Comparative Evaluation of Approaches to Propositionalization.
ILP 2003: 197-214 |
| 40 | EE | Dimitrios Mavroeidis,
Peter A. Flach:
Improved Distances for Structured Data.
ILP 2003: 251-268 |
| 2002 |
|---|
| 39 | EE | Peter A. Flach,
Nada Lavrac:
Learning in Clausal Logic: A Perspective on Inductive Logic Programming.
Computational Logic: Logic Programming and Beyond 2002: 437-471 |
| 38 | EE | Yonghong Peng,
Peter A. Flach,
Carlos Soares,
Pavel Brazdil:
Improved Dataset Characterisation for Meta-learning.
Discovery Science 2002: 141-152 |
| 37 | EE | Nada Lavrac,
Peter A. Flach,
Branko Kavsek,
Ljupco Todorovski:
Adapting classification rule induction to subgroup discovery.
ICDM 2002: 266-273 |
| 36 | | César Ferri,
Peter A. Flach,
José Hernández-Orallo:
Learning Decision Trees Using the Area Under the ROC Curve.
ICML 2002: 139-146 |
| 35 | | Thomas Gärtner,
Peter A. Flach,
Adam Kowalczyk,
Alex J. Smola:
Multi-Instance Kernels.
ICML 2002: 179-186 |
| 34 | EE | Nicolas Lachiche,
Peter A. Flach:
1BC2: A True First-Order Bayesian Classifier.
ILP 2002: 133-148 |
| 33 | EE | Nada Lavrac,
Filip Zelezný,
Peter A. Flach:
RSD: Relational Subgroup Discovery through First-Order Feature Construction.
ILP 2002: 149-165 |
| 32 | EE | Thomas Gärtner,
John W. Lloyd,
Peter A. Flach:
Kernels for Structured Data.
ILP 2002: 66-83 |
| 31 | EE | Tanja Urbancic,
Maja Skrjanc,
Peter A. Flach:
Web-based analysis of data mining and decision support education.
AI Commun. 15(4): 199-204 (2002) |
| 2001 |
|---|
| 30 | | Luc De Raedt,
Peter A. Flach:
Machine Learning: EMCL 2001, 12th European Conference on Machine Learning, Freiburg, Germany, September 5-7, 2001, Proceedings
Springer 2001 |
| 29 | EE | Peter A. Flach:
Multi-relational Data Mining: a perspective.
EPIA 2001: 3-4 |
| 28 | | Thomas Gärtner,
Peter A. Flach:
WBCsvm: Weighted Bayesian Classification based on Support Vector Machines.
ICML 2001: 154-161 |
| 27 | EE | Nada Lavrac,
Peter A. Flach:
An extended transformation approach to inductive logic programming.
ACM Trans. Comput. Log. 2(4): 458-494 (2001) |
| 26 | EE | Peter A. Flach:
On the state of the art in machine learning: A personal review.
Artif. Intell. 131(1-2): 199-222 (2001) |
| 25 | | Peter A. Flach,
Nicolas Lachiche:
Confirmation-Guided Discovery of First-Order Rules with Tertius.
Machine Learning 42(1/2): 61-95 (2001) |
| 24 | | Peter A. Flach,
Saso Dzeroski:
Editorial: Inductive Logic Programming is Coming of Age.
Machine Learning 44(3): 207-209 (2001) |
| 2000 |
|---|
| 23 | EE | Peter A. Flach,
Nicolas Lachiche:
Decomposing Probability Distributions on Structured Individuals.
ILP Work-in-progress reports 2000 |
| 22 | EE | Ljupco Todorovski,
Peter A. Flach,
Nada Lavrac:
Predictive Performance of Weghted Relative Accuracy.
PKDD 2000: 255-264 |
| 21 | | Peter A. Flach:
The Use of Functional and Logic Languages in Machine Learning.
WFLP 2000: 225-237 |
| 20 | EE | Iztok Savnik,
Peter A. Flach:
Discovery of multivalued dependencies from relations.
Intell. Data Anal. 4(3-4): 195-211 (2000) |
| 1999 |
|---|
| 19 | | Saso Dzeroski,
Peter A. Flach:
Inductive Logic Programming, 9th International Workshop, ILP-99, Bled, Slovenia, June 24-27, 1999, Proceedings
Springer 1999 |
| 18 | EE | Peter A. Flach:
Knowledge Representation for Inductive Learning.
ESCQARU 1999: 160-167 |
| 17 | EE | Nada Lavrac,
Peter A. Flach,
Blaz Zupan:
Rule Evaluation Measures: A Unifying View.
ILP 1999: 174-185 |
| 16 | EE | Peter A. Flach,
Nicolas Lachiche:
IBC: A First-Order Bayesian Classifier.
ILP 1999: 92-103 |
| 15 | | Peter A. Flach,
Iztok Savnik:
Database Dependency Discovery: A Machine Learning Approach.
AI Commun. 12(3): 139-160 (1999) |
| 1998 |
|---|
| 14 | | Peter A. Flach,
Christophe G. Giraud-Carrier,
John W. Lloyd:
Strongly Typed Inductive Concept Learning.
ILP 1998: 185-194 |
| 13 | | Peter A. Flach:
Comparing Consequence Relations.
KR 1998: 180-189 |
| 12 | EE | Peter A. Flach:
From Extensional to Intensional Knowledge: Inductive Logic Programming Techniques and Their Application to Deductive Databases.
Transactions and Change in Logic Databases 1998: 356-387 |
| 11 | EE | Peter A. Flach,
Antonis C. Kakas:
Abduction and Induction in AI: Report of the IJCAI'97 Workshop.
Logic Journal of the IGPL 6(4): 651-656 (1998) |
| 1997 |
|---|
| 10 | | Peter A. Flach:
Inductive Logic Databases: From Extensional to Intensional Knowledge.
DOOD 1997: 3 |
| 9 | | Peter A. Flach:
Normal Forms for Inductive Logic Programming.
ILP 1997: 149-156 |
| 8 | EE | Peter A. Flach,
Antonis C. Kakas:
Abductive and Inductive Reasoning: Report of the ECAI'96 Workshop.
Logic Journal of the IGPL 5(5): (1997) |
| 1996 |
|---|
| 7 | | Peter A. Flach:
Rationality Postulates for Induction.
TARK 1996: 267-281 |
| 1993 |
|---|
| 6 | | Peter A. Flach:
Predicate Invention in Inductive Data Engineering.
ECML 1993: 83-94 |
| 1992 |
|---|
| 5 | | Peter A. Flach:
An Analysis of Various Forms of "Jumping to Conclusions".
AII 1992: 170-186 |
| 4 | | Peter A. Flach:
A Model of Inductive Reasoning.
Logic at Work 1992: 41-56 |
| 1991 |
|---|
| 3 | | Shan-Hwei Nienhuys-Cheng,
Peter A. Flach:
Consistent Term Mappings, Term Partitions and Inverse Resolution.
EWSL 1991: 361-374 |
| 2 | | Peter A. Flach:
Towards a Theory of Inductive Logic Programming.
ISMIS 1991: 510-519 |
| 1989 |
|---|
| 1 | | Peter A. Flach:
Second-order Inductive Learning.
AII 1989: 202-216 |
