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» Learning aspect models with partially labeled data
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ICDM
2009
IEEE
233views Data Mining» more  ICDM 2009»
14 years 2 months ago
Semi-Supervised Sequence Labeling with Self-Learned Features
—Typical information extraction (IE) systems can be seen as tasks assigning labels to words in a natural language sequence. The performance is restricted by the availability of l...
Yanjun Qi, Pavel Kuksa, Ronan Collobert, Kunihiko ...
AAAI
2010
13 years 9 months ago
Facial Age Estimation by Learning from Label Distributions
One of the main difficulties in facial age estimation is the lack of sufficient training data for many ages. Fortunately, the faces at close ages look similar since aging is a slo...
Xin Geng, Kate Smith-Miles, Zhi-Hua Zhou
ICML
2004
IEEE
14 years 26 days ago
Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs
We introduce a new perceptron-based discriminative learning algorithm for labeling structured data such as sequences, trees, and graphs. Since it is fully kernelized and uses poin...
Hisashi Kashima, Yuta Tsuboi
ICIP
2010
IEEE
13 years 5 months ago
Semi-supervised regression with temporal image sequences
We consider a semi-supervised regression setting where we have temporal sequences of partially labeled data, under the assumption that the labels should vary slowly along a sequen...
Ling Xie, Miguel Á. Carreira-Perpiñ&...
SDM
2011
SIAM
233views Data Mining» more  SDM 2011»
12 years 10 months ago
Multi-Instance Mixture Models
Multi-instance (MI) learning is a variant of supervised learning where labeled examples consist of bags (i.e. multi-sets) of feature vectors instead of just a single feature vecto...
James R. Foulds, Padhraic Smyth