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» GraphLab: A New Framework for Parallel Machine Learning
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ICML
2005
IEEE
14 years 8 months ago
Clustering through ranking on manifolds
Clustering aims to find useful hidden structures in data. In this paper we present a new clustering algorithm that builds upon the consistency method (Zhou, et.al., 2003), a semi-...
Markus Breitenbach, Gregory Z. Grudic
ICCV
2009
IEEE
13 years 5 months ago
A robust boosting tracker with minimum error bound in a co-training framework
The varying object appearance and unlabeled data from new frames are always the challenging problem in object tracking. Recently machine learning methods are widely applied to tra...
Rong Liu, Jian Cheng, Hanqing Lu
ICIP
2008
IEEE
14 years 2 months ago
Gradual transition detection using color coherence and other criteria in a video shot meta-segmentation framework
Shot segmentation provides the basis for almost all high-level video content analysis approaches, validating it as one of the major prerequisites for efficient video semantic ana...
Efthymia Tsamoura, Vasileios Mezaris, Ioannis Komp...
JMLR
2010
121views more  JMLR 2010»
13 years 2 months ago
Sparse Semi-supervised Learning Using Conjugate Functions
In this paper, we propose a general framework for sparse semi-supervised learning, which concerns using a small portion of unlabeled data and a few labeled data to represent targe...
Shiliang Sun, John Shawe-Taylor
CMMR
2004
Springer
118views Music» more  CMMR 2004»
14 years 1 months ago
Methods for Combining Statistical Models of Music
Abstract. The paper concerns the use of multiple viewpoint representation schemes for prediction with statistical models of monophonic music. We present an experimental comparison ...
Marcus Pearce, Darrell Conklin, Geraint A. Wiggins