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ICPR
2010
IEEE
13 years 4 months ago
A Multiple Instance Learning Approach toward Optimal Classification of Pathology Slides
Murat Dundar, Sunil Badve, Vikas C. Raykar, Rohit ...
ECCV
2010
Springer
13 years 4 months ago
MIForests: Multiple-Instance Learning with Randomized Trees
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...
Christian Leistner, Amir Saffari, Horst Bischof
CVPR
2009
IEEE
14 years 12 months ago
A Min-Max Framework of Cascaded Classifier with Multiple Instance Learning for Computer Aided Diagnosis
The computer aided diagnosis (CAD) problems of detecting potentially diseased structures from medical images are typically distinguished by the following challenging characterist...
Dijia Wu (Rensselaer Polytechnic Institute), Jinbo...
CIKM
2008
Springer
13 years 6 months ago
Proactive learning: cost-sensitive active learning with multiple imperfect oracles
Proactive learning is a generalization of active learning designed to relax unrealistic assumptions and thereby reach practical applications. Active learning seeks to select the m...
Pinar Donmez, Jaime G. Carbonell
IJDMMM
2008
124views more  IJDMMM 2008»
13 years 4 months ago
Completing missing views for multiple sources of web media
: Combining multiple data sources, each with its own features, to achieve optimal inference has received a lot of attention in recent years. In inference from multiple data sources...
Shankara B. Subramanya, Zheshen Wang, Baoxin Li, H...