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156
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WWW
2011
ACM
14 years 10 months ago
Learning to rank with multiple objective functions
We investigate the problem of learning to rank for document retrieval from the perspective of learning with multiple objective functions. We present solutions to two open problems...
Krysta Marie Svore, Maksims Volkovs, Christopher J...
137
Voted
ECCV
2010
Springer
15 years 3 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
138
Voted
SIGIR
2006
ACM
15 years 9 months ago
Latent semantic analysis for multiple-type interrelated data objects
Co-occurrence data is quite common in many real applications. Latent Semantic Analysis (LSA) has been successfully used to identify semantic relations in such data. However, LSA c...
Xuanhui Wang, Jian-Tao Sun, Zheng Chen, ChengXiang...
117
Voted
ICML
2006
IEEE
16 years 4 months ago
Combined central and subspace clustering for computer vision applications
Central and subspace clustering methods are at the core of many segmentation problems in computer vision. However, both methods fail to give the correct segmentation in many pract...
Le Lu, René Vidal
131
Voted
IPMI
1999
Springer
15 years 8 months ago
Model Generation from Multiple Volumes Using Constrained Elastic SurfaceNets
Three dimensional models of anatomical structures are currently used to aid in medical diagnosis, treatment, surgical guidance, and surgical simulation. Limitations on the resolut...
Michael E. Leventon, Sarah F. Frisken Gibson