Literature  and Datasets for the Multiple Instance Problem

Papers
Theses
Datasets
Other related work

Please send corrections and suggestions for additional papers and data to  sray_AT_cs_dot_wisc.edu or sray_AT_biostat_dot_wisc.edu . The list of papers in particular is out of date and will be updated as I find time or get email about specific papers that should be added.

Papers
  1. Solving the Multiple-Instance Problem with Axis-Parallel.. - Dietterich, Lathrop (1997)
  2. A Framework for Multiple-Instance Learning - Maron, Lozano-Pérez (1998)
  3. Multiple-Instance Learning for Natural Scene Classification - Maron, Ratan (1998)
  4. A Note on Learning from Multiple-Instance Examples - Blum, Kalai (1998)
  5. Approximating Hyper-Rectangles: Learning and Pseudo-random Sets - Auer, Long, al. (1997)
  6. On Learning from Multi-Instance Examples: Empirical Evaluation of a theoretical approach- Auer (1997)
  7. Pharmacophore Discovery using the Inductive Logic Programming System PROGOL- Paul Finn, Muggleton, Page, Srinivasan (1998)
  8. Multiple Instance Regression - Ray, Page (2001)
  9. 1BC: a First-Order Bayesian Classifier - Flach, Lachiche (1999)
  10. Image Database Retrieval With Multiple-Instance Learning Techniques - Yang (2000)
  11. Solving the Multiple-Instance Problem: A Lazy Learning Approach - Wang, al. (2000)
  12. Decomposing probability distributions on structured individuals - Flach, Lachiche (2000)
  13. Learning Structurally Indeterminate Clauses - Zucker, Ganascia (1998)
  14. Agnostic Learning of Geometric Patterns - Goldman, Kwek, Scott (1997)
  15. Content-Based Image Retrieval Using Multiple-Instance Learning - Qi Zhang
  16. EM-DD: An Improved Multiple-Instance Learning Technique - Qi Zhang
  17. Four Suggestions and a Rule Concerning the Application of ILP - Ashwin Srinivasan
  18. Physically grounding the lexical semantics of words in a.. - Bredeche, Chevaleyre (2002)
  19. A Framework for Learning Rules from Multiple Instance Data - Chevaleyre, Zucker (2001)
  20. Noise-Tolerant Rule induction from Multi-Instance data - Yann Chevaleyre, Zucker
  21. Automatic Discovery of Subgoals in Reinforcement Learning.. - McGovern, Barto (2001)
  22. Multiple-Instance Learning of Real-Valued Data - Amar, Dooly, Goldman, Zhang (2001)
  23. Solving multiple-instance and multiple-part learning problems with .. - Yann
  24. An Empirical Approach To Real-Valued Multiple-Instance.. - Amar (2000)
  25. Multiple-Instance Learning of Real-Valued Geometric Patterns - Goldman, Scott (2000)
  26. A Unifying View of Knowledge Representation for.. - Bowers, Giraud-Carrier, .. (2000)
  27. A Case Study in Machine Learning for Combinatorial Chemistry - Page, Curtis, Graham..
  28. Geometric Patterns: Algorithms and Applications - Scott (2000)
  29. Mining the Web for Object Recognition - Charles Rosenberg
  30. Event Prediction: Learning from Ambiguous Examples - Gary Weiss
  31. An Inductive Logic Programming Framework to Learn a Concept .. - Bouthinon, Soldano (1998)
  32. Multi Instance Neural Networks - Ramon, De Raedt (2000)
  33. Attribute Value Learning vs ILP: the missing links (De Raedt, 2000)
  34. Learning to Rank Structured Alternatives: An.. - Costa, Frasconi..
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Theses
  1. Learning from Ambiguity - Maron (1998)
  2. Exploring Applications of Learning Theory to Pattern Matching and.. - Scott (1998)
  3. Learning single and multiple instance decision trees for computer security applications. Ruffo, G (2000)
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Other Related Work (from Oded Maron's old MI Page)
 
  1.    Phoneme Recognition Using Time-Delay Neural Networks, by Alexander Waibel, Toshiyuki Hanazawa, Geoffrey Hinton, Kiyohiro Shikano, and Kevin J. Lang, in IEEE Transactions on Acoustics, Speech, and Signal-Processing, 37:3, 1989.
  2. Integrated Segmentation and Recognition of Hand-Printed Numerals, by James D. Keeler, David E. Rumelhart, and Wee-Kheng Leow, in Neural Information Processing Systems 3, 1991.
  3. Learning from Data with Bounded Inconsistency, by Haym Hirsh, in International Conference on Machine Learning (ML-90), 1990.
  4. Learning to Recognize Promoter Sequences in E. Coli by Modeling Uncertainty in the Training Data, by Steve W. Norton, in Proceedings, Twelfth National Conference on Artificial Intelligence (AAAI-94), 1994.
  5. Compass: A shape-based machine learning tool for drug design, by Jain, A. N., Dietterich, T. G., Lathrop, R. H., Chapman, D., Critchlow, R. E., Bauer, B. E., Webster, T. A., Lozano-Perez, T. in Computer-Aided Molecular Design, 8 (6) 635--652, 1994.
  6.   A comparison of dynamic reposing and tangent distance for drug activity prediction, by Dietterich, T. G., Jain, A., Lathrop, R., Lozano-Perez, T. in Advances in Neural Information Processing Systems, 6. San Mateo, CA: Morgan Kaufmann. 216-223, 1994. Postscript preprint.
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MI Classification Datasets
  1. MUSK
  2. Image data used by Maron, Goldman, Andrews and others
  3. Reinforcement Learning agent simulator
  4. Chinese characters
  5. Text Categorization
  6. Protein classification
MI Regression Datasets
  1. Synthetic data from Sally Goldman
  2. Thermolysin inhibitors (coming soon)
  3. Thrombin inhibitors (coming soon)
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Prepared by Soumya Ray. Last Update 9/26/2005.