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AAAI
2010
15 years 5 months ago
Multi-Label Learning with Weak Label
Multi-label learning deals with data associated with multiple labels simultaneously. Previous work on multi-label learning assumes that for each instance, the "full" lab...
Yu-Yin Sun, Yin Zhang, Zhi-Hua Zhou
148
Voted
MM
2004
ACM
248views Multimedia» more  MM 2004»
15 years 9 months ago
Incremental semi-supervised subspace learning for image retrieval
Subspace learning techniques are widespread in pattern recognition research. They include Principal Component Analysis (PCA), Locality Preserving Projection (LPP), etc. These tech...
Xiaofei He
AIED
2007
Springer
15 years 10 months ago
Accelerated Future Learning via Explicit Instruction of a Problem Solving Strategy
Explicit instruction in a problem-solving strategy accelerated learning not only in the domain where it was taught but also in a second domain where it was not taught. We present d...
Min Chi, Kurt VanLehn
155
Voted
NIPS
2001
15 years 5 months ago
Model-Free Least-Squares Policy Iteration
We propose a new approach to reinforcement learning which combines least squares function approximation with policy iteration. Our method is model-free and completely off policy. ...
Michail G. Lagoudakis, Ronald Parr
CVPR
2004
IEEE
16 years 6 months ago
Local Smoothing for Manifold Learning
We propose methods for outlier handling and noise reduction using weighted local linear smoothing for a set of noisy points sampled from a nonlinear manifold. The methods can be u...
Jin Hyeong Park, Zhenyue Zhang, Hongyuan Zha, Rang...