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126
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CVPR
2010
IEEE
15 years 3 months ago
Boosting for transfer learning with multiple sources
Transfer learning allows leveraging the knowledge of source domains, available a priori, to help training a classifier for a target domain, where the available data is scarce. Th...
Yi Yao, Gianfranco Doretto
121
Voted
JMLR
2006
120views more  JMLR 2006»
15 years 3 months ago
Learning Parts-Based Representations of Data
Many perceptual models and theories hinge on treating objects as a collection of constituent parts. When applying these approaches to data, a fundamental problem arises: how can w...
David A. Ross, Richard S. Zemel
127
Voted
CE
2007
96views more  CE 2007»
15 years 3 months ago
Mobile learning: A framework and evaluation
Wireless data communications in form of Short Message Service (SMS) and Wireless Access Protocols (WAP) browsers have gained global popularity, yet, not much has been done to exte...
Luvai F. Motiwalla
136
Voted
IJON
2010
119views more  IJON 2010»
15 years 2 months ago
Hyperparameter learning in probabilistic prototype-based models
We present two approaches to extend Robust Soft Learning Vector Quantization (RSLVQ). This algorithm for nearest prototype classification is derived from an explicit cost functio...
Petra Schneider, Michael Biehl, Barbara Hammer
BMVC
2010
15 years 1 months ago
StyP-Boost: A Bilinear Boosting Algorithm for Learning Style-Parameterized Classifiers
We introduce a novel bilinear boosting algorithm, which extends the multi-class boosting framework of JointBoost to optimize a bilinear objective function. This allows style param...
Jonathan Warrell, Philip H. S. Torr, Simon Prince