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ICCV
2007
IEEE
16 years 2 months ago
Learning to Find Object Boundaries Using Motion Cues
While great strides have been made in detecting and localizing specific objects in natural images, the bottom-up segmentation of unknown, generic objects remains a difficult chall...
Andrew N. Stein, Derek Hoiem, Martial Hebert
90
Voted
KDD
2003
ACM
127views Data Mining» more  KDD 2003»
16 years 25 days ago
Experiments with random projections for machine learning
Dimensionality reduction via Random Projections has attracted considerable attention in recent years. The approach has interesting theoretical underpinnings and offers computation...
Dmitriy Fradkin, David Madigan
97
Voted
ACL
2007
15 years 1 months ago
A Seed-driven Bottom-up Machine Learning Framework for Extracting Relations of Various Complexity
A minimally supervised machine learning framework is described for extracting relations of various complexity. Bootstrapping starts from a small set of n-ary relation instances as...
Feiyu Xu, Hans Uszkoreit, Hong Li
ICML
2008
IEEE
16 years 1 months ago
A decoupled approach to exemplar-based unsupervised learning
A recent trend in exemplar based unsupervised learning is to formulate the learning problem as a convex optimization problem. Convexity is achieved by restricting the set of possi...
Gökhan H. Bakir, Sebastian Nowozin
105
Voted
ECML
2005
Springer
15 years 6 months ago
Using Advice to Transfer Knowledge Acquired in One Reinforcement Learning Task to Another
We present a method for transferring knowledge learned in one task to a related task. Our problem solvers employ reinforcement learning to acquire a model for one task. We then tra...
Lisa Torrey, Trevor Walker, Jude W. Shavlik, Richa...