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» Learning from Highly Structured Data by Decomposition
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ICML
2002
IEEE
15 years 11 months ago
Combining Labeled and Unlabeled Data for MultiClass Text Categorization
Supervised learning techniques for text classi cation often require a large number of labeled examples to learn accurately. One way to reduce the amountoflabeled datarequired is t...
Rayid Ghani
NIPS
2004
14 years 11 months ago
Non-Local Manifold Tangent Learning
We claim and present arguments to the effect that a large class of manifold learning algorithms that are essentially local and can be framed as kernel learning algorithms will suf...
Yoshua Bengio, Martin Monperrus
CAIP
2009
Springer
209views Image Analysis» more  CAIP 2009»
15 years 4 months ago
Smooth Multi-Manifold Embedding for Robust Identity-Independent Head Pose Estimation
In this paper, we propose a supervised Smooth Multi-Manifold Embedding (SMME) method for robust identity-independent head pose estimation. In order to handle the appearance variati...
Xiangyang Liu, Hongtao Lu, Heng Luo
KDD
2008
ACM
161views Data Mining» more  KDD 2008»
15 years 10 months ago
Spectral domain-transfer learning
Traditional spectral classification has been proved to be effective in dealing with both labeled and unlabeled data when these data are from the same domain. In many real world ap...
Xiao Ling, Wenyuan Dai, Gui-Rong Xue, Qiang Yang, ...
97
Voted
CVPR
1999
IEEE
16 years 4 days ago
Time-Series Classification Using Mixed-State Dynamic Bayesian Networks
We present a novel mixed-state dynamic Bayesian network (DBN) framework for modeling and classifying timeseries data such as object trajectories. A hidden Markov model (HMM) of di...
Vladimir Pavlovic, Brendan J. Frey, Thomas S. Huan...