Abstract is missing.
- Probabilistic Relational ModelsDaphne Koller. 3-13 [doi]
- Inductive Databases (Abstract)Heikki Mannila. 14 [doi]
- Some Elements of Machine Learning (Extended Abstract)J. Ross Quinlan. 15-18 [doi]
- Refinement Operators Can Be (Weakly) PerfectLiviu Badea, Monica Stanciu. 21-32 [doi]
- Combining Divide-and-Conquer and Separate-and-Conquer for Efficient and Effective Rule InductionHenrik Boström, Lars Asker. 33-43 [doi]
- Refining Complete Hypotheses in ILPIvan Bratko. 44-55 [doi]
- Acquiring Graphic Design Knowledge with Nonmonotonic Inductive LearningKazuya Chiba, Hayato Ohwada, Fumio Mizoguchi. 56-67 [doi]
- Morphosyntactic Tagging of Slovene Using ProgolJames Cussens, Saso Dzeroski, Tomaz Erjavec. 68-79 [doi]
- Experiments in Predicting BiodegradabilitySaso Dzeroski, Hendrik Blockeel, Boris Kompare, Stefan Kramer, Bernhard Pfahringer, Wim Van Laer. 80-91 [doi]
- IBC: A First-Order Bayesian ClassifierPeter A. Flach, Nicolas Lachiche. 92-103 [doi]
- Sorted Downward Refinement: Building Background Knowledge into a Refinement Operator for Inductive ProgrammingAlan M. Frisch. 104-115 [doi]
- A Strong Complete Schmema for Inductive Functional Logic ProgrammingJosé Hernández-Orallo, M. José Ramírez-Quintana. 116-127 [doi]
- Application of Different Learning Methods to Hungarian Part-of-Speech TaggingTamás Horváth, Zoltán Alexin, Tibor Gyimóthy, Stefan Wrobel. 128-139 [doi]
- Combining LAPIS and WordNet for Learning of LR Parsers with Optimal Semantic ConstraintsDimitar Kazakov. 140-151 [doi]
- Learning Word Segmentation Rules for Tag PredictionDimitar Kazakov, Suresh Manandhar, Tomaz Erjavec. 152-161 [doi]
- Approximate ILP Rules by Backpropagation Neural Network: A Result on Thai Character RecognitionBoonserm Kijsirikul, Sukree Sinthupinyo. 162-173 [doi]
- Rule Evaluation Measures: A Unifying ViewNada Lavrac, Peter A. Flach, Blaz Zupan. 174-185 [doi]
- Improving Part of Speech Disambiguation Rules by Adding Linguistic KnowledgeNikolaj Lindberg, Martin Eineborg. 186-197 [doi]
- On Sufficient Conditions for Learnability of Logic Programs from Positive DataEric Martin, Arun Sharma. 198-209 [doi]
- A Bounded Search Space of Clausal TheoriesHerman Midelfart. 210-221 [doi]
- Discovering New Knowledge from Graph Data Using Inductive Logic ProgrammingTetsuhiro Miyahara, Takayoshi Shoudai, Tomoyuki Uchida, Tetsuji Kuboyama, Kenichi Takahashi, Hiroaki Ueda. 222-233 [doi]
- Analogical PredictionStephen Muggleton, Michael Bain. 234-244 [doi]
- Generalizing Refinement Operators to Learn Prenex Conjunctive Normal FormsShan-Hwei Nienhuys-Cheng, Wim Van Laer, Jan Ramon, Luc De Raedt. 245-256 [doi]
- Theory RecoveryRupert Parson, Khalid Khan, Stephen Muggleton. 257-267 [doi]
- Instance Based Function LearningJan Ramon, Luc De Raedt. 268-278 [doi]
- Some Properties of Invers Resolution in Normal Logic ProgramsChiaki Sakama. 279-290 [doi]
- An Assessment of ILP-Assisted Models for Toxicology and the PTE-3 ExperimentAshwin Srinivasan, Ross D. King, Douglas W. Bristol. 291-302 [doi]