Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Approximating hyper-rectangles: learning and pseudo-random sets
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
A Note on Learning from Multiple-Instance Examples
Machine Learning - Special issue on the ninth annual conference on computational theory (COLT '96)
Scaling Up Inductive Logic Programming by Learning from Interpretations
Data Mining and Knowledge Discovery
Some Lower Bounds for the Computational Complexity of Inductive Logic Programming
ECML '93 Proceedings of the European Conference on Machine Learning
On Learning From Multi-Instance Examples: Empirical Evaluation of a Theoretical Approach
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Analyzing Relational Learning in the Phase Transition Framework
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning Structurally Indeterminate Clauses
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Attribute-Value Learning Versus Inductive Logic Programming: The Missing Links (Extended Abstract)
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Solving the Multiple-Instance Problem: A Lazy Learning Approach
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Tractable induction and classification in first order logic via stochastic matching
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Abstracting Visual Percepts to Learn Concepts
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
A Pathology of Bottom-Up Hill-Climbing in Inductive Rule Learning
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
Multi-objective Genetic Programming for Multiple Instance Learning
ECML '07 Proceedings of the 18th European conference on Machine Learning
Multi-instance multi-label learning
Artificial Intelligence
Mining chemical compound structure data using inductive logic programming
AM'03 Proceedings of the Second international conference on Active Mining
Support vector inductive logic programming
DS'05 Proceedings of the 8th international conference on Discovery Science
| Hi-index | 0.00 |
This paper proposes a generic extension to propositional rule learners to handle multiple-instance data. In a multiple-instance representation, each learning example is represented by a "bag" of fixed-length "feature vectors". Such a representation, lying somewhere between propositional and first-order representation, offers a tradeoff between the two. NAIVE-RIPPERMI is one implementation of this extension on the rule learning algorithm RIPPER. Several pitfalls encountered by this naive extension during induction are explained. A new multiple-instance search bias based on decision tree techniques is then used to avoid these pitfalls. Experimental results show the benefits of this approach for solving propositionalized relational problems in terms of speed and accuracy.