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123
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ICPR
2008
IEEE
16 years 4 months ago
Incremental learning in non-stationary environments with concept drift using a multiple classifier based approach
We outline an incremental learning algorithm designed for nonstationary environments where the underlying data distribution changes over time. With each dataset drawn from a new e...
Matthew T. Karnick, Michael Muhlbaier, Robi Polika...
166
Voted
FOIKS
2008
Springer
15 years 11 months ago
Cost-minimising strategies for data labelling : optimal stopping and active learning
Supervised learning deals with the inference of a distribution over an output or label space $\CY$ conditioned on points in an observation space $\CX$, given a training dataset $D$...
Christos Dimitrakakis, Christian Savu-Krohn
113
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ICML
2009
IEEE
15 years 9 months ago
Active learning for directed exploration of complex systems
Physics-based simulation codes are widely used in science and engineering to model complex systems that would be infeasible to study otherwise. Such codes provide the highest-fid...
Michael C. Burl, Esther Wang
113
Voted
ICDM
2007
IEEE
187views Data Mining» more  ICDM 2007»
15 years 9 months ago
A Comparative Study of Methods for Transductive Transfer Learning
The problem of transfer learning, where information gained in one learning task is used to improve performance in another related task, is an important new area of research. While...
Andrew Arnold, Ramesh Nallapati, William W. Cohen
135
Voted
ICCV
2005
IEEE
15 years 8 months ago
LOCUS: Learning Object Classes with Unsupervised Segmentation
We address the problem of learning object class models and object segmentations from unannotated images. We introduce LOCUS (Learning Object Classes with Unsupervised Segmentation...
John M. Winn, Nebojsa Jojic