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» Learning classifiers from only positive and unlabeled data
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CVPR
2006
IEEE
14 years 10 months ago
Graph Laplacian Kernels for Object Classification from a Single Example
Classification with only one labeled example per class is a challenging problem in machine learning and pattern recognition. While there have been some attempts to address this pr...
Hong Chang, Dit-Yan Yeung
SEKE
2007
Springer
14 years 2 months ago
Adjudicator: A Statistical Approach for Learning Ontology Concepts from Peer Agents
— We present a statistical approach for software agents to learn ontology concepts from peer agents by asking them whether they can reach consensus on significant differences bet...
Behrouz Homayoun Far, Abdel Halim Elamy, Nora Houa...
FLAIRS
2008
13 years 11 months ago
Machine Learning to Predict the Incidence of Retinopathy of Prematurity
Retinopathy of Prematurity (ROP) is a disorder afflicting prematurely born infants. ROP can be positively diagnosed a few weeks after birth. The goal of this study is to build an ...
Aniket Ray, Vikas Kumar, Balaraman Ravindran, Ling...
CVPR
2007
IEEE
14 years 10 months ago
Combining Static Classifiers and Class Syntax Models for Logical Entity Recognition in Scanned Historical Documents
Class syntax can be used to 1) model temporal or locational evolvement of class labels of feature observation sequences, 2) correct classification errors of static classifiers if ...
Song Mao, Praveer Mansukhani, George R. Thoma
ECCV
2008
Springer
14 years 10 months ago
Multiple Component Learning for Object Detection
Abstract. Object detection is one of the key problems in computer vision. In the last decade, discriminative learning approaches have proven effective in detecting rigid objects, a...
Boris Babenko, Pietro Perona, Piotr Dollár,...