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CVPR
2010
IEEE
14 years 3 months ago
Online Multiple Instance Learning with No Regret
Multiple instance (MI) learning is a recent learning paradigm that is more flexible than standard supervised learning algorithms in the handling of label ambiguity. It has been u...
Li Mu, James Kwok, Lu Bao-liang
CVPR
2010
IEEE
14 years 3 months ago
On-line Semi-supervised Multiple-Instance Boosting
A recent dominating trend in tracking called tracking-by-detection uses on-line classifiers in order to redetect objects over succeeding frames. Although these methods usually deli...
Bernhard Zeisl, Christian Leistner, Amir Saffari, ...
PAKDD
2009
ACM
94views Data Mining» more  PAKDD 2009»
14 years 2 months ago
When does Co-training Work in Real Data?
Co-training, a paradigm of semi-supervised learning, may alleviate effectively the data scarcity problem (i.e., the lack of labeled examples) in supervised learning. The standard ...
Charles X. Ling, Jun Du, Zhi-Hua Zhou
DAGM
2009
Springer
14 years 2 months ago
Multi-view Object Detection Based on Spatial Consistency in a Low Dimensional Space
This paper describes a new approach for detecting objects based on measuring the spatial consistency between different parts of an object. These parts are pre-defined on a set of...
Gurman Gill, Martin Levine
ICDM
2008
IEEE
136views Data Mining» more  ICDM 2008»
14 years 2 months ago
Document-Word Co-regularization for Semi-supervised Sentiment Analysis
The goal of sentiment prediction is to automatically identify whether a given piece of text expresses positive or negative opinion towards a topic of interest. One can pose sentim...
Vikas Sindhwani, Prem Melville