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» A Framework for Multiple-Instance Learning
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ICML
2005
IEEE
16 years 4 months ago
Compact approximations to Bayesian predictive distributions
We provide a general framework for learning precise, compact, and fast representations of the Bayesian predictive distribution for a model. This framework is based on minimizing t...
Edward Snelson, Zoubin Ghahramani
KES
2007
Springer
15 years 10 months ago
A Hybrid Symbolic-Statistical Approach to Modeling Metabolic Networks
Biological systems consist of many components and interactions between them. In Systems Biology the principal problem is modeling complex biological systems and reconstructing inte...
Marenglen Biba, Stefano Ferilli, Nicola Di Mauro, ...
ICCBR
2005
Springer
15 years 9 months ago
Selecting the Best Units in a Fleet: Performance Prediction from Equipment Peers
We focus on the problem of selecting the few vehicles in a fleet that are expected to last the longest without failure. The prediction of each vehicle’s remaining life is based o...
Anil Varma, Kareem S. Aggour, Piero P. Bonissone
ICDM
2002
IEEE
70views Data Mining» more  ICDM 2002»
15 years 8 months ago
Progressive Modeling
Presently, inductive learning is still performed in a frustrating batch process. The user has little interaction with the system and no control over the final accuracy and traini...
Wei Fan, Haixun Wang, Philip S. Yu, Shaw-hwa Lo, S...
ICML
1994
IEEE
15 years 7 months ago
Combining Top-down and Bottom-up Techniques in Inductive Logic Programming
This paper describes a new methodfor inducing logic programs from examples which attempts to integrate the best aspects of existingILP methodsintoa singlecoherent framework. In pa...
John M. Zelle, Raymond J. Mooney, Joshua B. Konvis...