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 |