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ICDM
2008
IEEE
97views Data Mining» more  ICDM 2008»
16 years 1 months ago
Semi-supervised Learning from General Unlabeled Data
We consider the problem of Semi-supervised Learning (SSL) from general unlabeled data, which may contain irrelevant samples. Within the binary setting, our model manages to better...
Kaizhu Huang, Zenglin Xu, Irwin King, Michael R. L...
ICSNC
2007
IEEE
16 years 28 days ago
Movement Prediction Using Bayesian Learning for Neural Networks
Nowadays, path prediction is being extensively examined for use in the context of mobile and wireless computing towards more efficient network resource management schemes. Path pr...
Sherif Akoush, Ahmed Sameh
AMFG
2005
IEEE
203views Biometrics» more  AMFG 2005»
16 years 7 days ago
Learning to Fuse 3D+2D Based Face Recognition at Both Feature and Decision Levels
2D intensity images and 3D shape models are both useful for face recognition, but in different ways. While algorithms have long been developed using 2D or 3D data, recently has see...
Stan Z. Li, ChunShui Zhao, Meng Ao, Zhen Lei
CIVR
2008
Springer
271views Image Analysis» more  CIVR 2008»
15 years 8 months ago
Multiple feature fusion by subspace learning
Since the emergence of extensive multimedia data, feature fusion has been more and more important for image and video retrieval, indexing and annotation. Existing feature fusion t...
Yun Fu, Liangliang Cao, Guodong Guo, Thomas S. Hua...
SDM
2010
SIAM
144views Data Mining» more  SDM 2010»
15 years 8 months ago
A Probabilistic Framework to Learn from Multiple Annotators with Time-Varying Accuracy
This paper addresses the challenging problem of learning from multiple annotators whose labeling accuracy (reliability) differs and varies over time. We propose a framework based ...
Pinar Donmez, Jaime G. Carbonell, Jeff Schneider