Benefitting from the variables that variable selection discards

Journal of Machine Learning Research

Volumn 3, Issue , 2003, Pages 1245-1264

  Caruana, Rich ^a   De Sa, Virginia R ^b  

^a Cornell University ^*  (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Inductive transfer; Multitask learning; Output variable selection

Indexed keywords

BETTER PERFORMANCE; INDUCTIVE TRANSFER; MULTITASK LEARNING; OUTPUT VARIABLES; REGRESSION PROBLEM; RISK PREDICTIONS; SYNTHETIC PROBLEM; VARIABLE SELECTION;

ARTIFICIAL INTELLIGENCE;

SOFTWARE ENGINEERING;

EID: 2942734703     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (27)

1. Y.S. Abu-Mostafa. Learning from hints in neural networks. Journal of Complexity, 6(2):192-198, 1989.
2. Shumeet Baluja and Dean A Pomerleau. Using the representation in a neural network's hidden layer for task-specific focus of attention. In C. Mellish, editor, The International Joint Conference on Artificial Intelligence 1995 (IJCAI-95), pages 133-139. IJCAI & Morgan Kaufmann, San Mateo, CA, 1995.
3. J. Baxter. Learning internal representations. In Proceedings of the Eleventh Annual Conference on Computational Learning Theory (COLT-95), 1995.
4. Suzanna Becker and Geoffrey E. Hinton. Spatial coherence as an internal teacher for a neural network. Technical Report CRG-TR-89-7, University of Toronto, Connectionist Research Group, December 1989.
5. C. M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, 1995.
6. R. Caruana, S. Baluja, and T. Mitchell. Using the future to sort out the present: Rankprop and multitask learning for pneumonia risk prediction. In D.S. Touretzky, M.C. Mozer, and M.E. Hasselmo, editors, Advances in Neural Information Processing Systems 8, pages 959-965. MIT Press, 1996.
7. Rich Caruana. Learning many related tasks at the same time with backpropagation. In G. Tesauro, D. Touretzky, and T. Leen, editors, Advances in Neural Information Processing Systems 7, pages 657-664. MIT Press, 1995.
8. Rich Caruana. Applications and algorithms for multitask learning. In ICML-96, pages 87-95, 1996.
9. Rich Caruana. Multitask learning. Machine Learning, 28:41-75, 1997a.
10. Rich Caruana. Multitask Learning. PhD thesis, Department of Computer Science, Carnegie Mellon University, 1997b. (available as CMU-CS-97-203).
11. Rich Caruana and Dayne Freitag. Greedy attribute selection. In ICML-94, pages 28-36, 1994.
12. G.F. Cooper, C.F. Aliferis, R. Ambrosino, J. Aronis, B.G. Buchanan, R. Caruana, M.J. Fine, C. Glymour, G. Gordon, B.H. Hanusa, J.E. Janosky, C.Meek, T. Mitchell, T. Richardson, and P. Spirtes. An evaluation of machine learning methods for predicting pneumonia mortality. Artificial Intelligence in Medicine, 9:107-138, 1997.
13. C. Cortes and V. Vapnik. Support-vector networks. Machine Learning, 20(3):273-297, 1995.
14. Virginia R. de Sa. Learning classification with unlabeled data. In J.D. Cowan, G. Tesauro, and J. Alspector, editors, Advances in Neural Information Processing Systems 6, pages 112-119. Morgan Kaufmann, 1994.
15. Virginia R. de Sa and Dana H. Ballard. Category learning through multimodality sensing. Neural Computation, 10(5):1097-1117, 1998.
16. T.G. Dietterich, H. Hild, and G. Bakiri. A comparison of id3 and backpropagation for english text-to-speech mapping. Machine Learning, 18(1):51-80, 1995.
17. Joumana Ghosn and Yoshua Bengio. Multi-task learning for stock selection. In Michael C. Mozer, Michael I. Jordan, and Thomas Petsche, editors, Advances in Neural Information Processing Systems 9, pages 946-952. The MIT Press, 1997.
18. Tony Jebara. Multi-task svms. presented at the Machines that Learn Workshop, Snowbird 2002 and available electronically at http://www.cs.columbia. edu/jebara/multitask.html., 2002.
19. G. John, R. Kohavi, and K. Pfleger. Irrelevant features and the subset selection problem. In ICML- 94, pages 121-129, 1994.
20. Daphne Koller and M. Sahami. Towards optimal feature selection. In ICML-96, pages 284-292, 1996.
21. M. Noordewier, G. Towell, and J. Shavlik. Training knowledge-based neural networks to recognize genes in dna sequences. In Richard Lippmann, John E. Moody, and David S. Touretzky, editors, Advances in Neural Information Processing Systems 3, [NIPS Conference, Denver, Colorado, USA, November 26-29, 1990], pages 530-536. Morgan Kaufmann, 1991.
22. National Institute of Standards and Technology. Gray code. available electronically at http://www.nist.gov/dads/HTML/graycode.html.
23. F. Provost, T. Fawcett, and Ron Kohavi. The case against accuracy estimation for comparing induction algorithms. In ICML-98, pages 445-553, 1998.
24. D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning representations by back-propagating errors. Nature, 323:533-536, 1986.
25. Patrice Simard, Bernard Victorri, Yann LeCun, and John Denker. Tangent prop - a formalism for specifying selected invariances in an adaptive network. In John E. Moody, Steve J. Hanson, and Richard P. Lippmann, editors, Advances in Neural Information Processing Systems 4, pages 895-903. Morgan Kaufmann Publishers, Inc., 1992.
26. S.C. Suddarth and A.D.C. Holden. Symbolic-neural systems and the use of hints for developing complex systems. International Journal of Man-Machine Studies, 35(3):291-311, 1991.
27. S. Thrun and T. Mitchell. Learning one more thing. Technical Report CS-94-184, Department of Computer Science, CMU, 1994.