750 search results - page 117 / 150 » Learning SVMs from Sloppily Labeled Data |
KDD
2006
ACM
14 years 10 months ago
2006
ACM
Many applications in text processing require significant human effort for either labeling large document collections (when learning statistical models) or extrapolating rules from...
KDD
2006
ACM
14 years 10 months ago
2006
ACM
Kernel machines have been shown as the state-of-the-art learning techniques for classification. In this paper, we propose a novel general framework of learning the Unified Kernel ...
ECCV
2008
Springer
15 years 9 days ago
2008
Springer
Multiple Instance Learning (MIL) provides a framework for training a discriminative classifier from data with ambiguous labels. This framework is well suited for the task of learni...
CVPR
2009
IEEE
15 years 5 months ago
2009
IEEE
Latent Variable Models (LVM), like the Shared-GPLVM
and the Spectral Latent Variable Model, help mitigate over-
fitting when learning discriminative methods from small or
modera...
WSDM
2012
ACM
12 years 6 months ago
2012
ACM
It is well known that different types of social ties have essentially different influence between people. However, users in online social networks rarely categorize their contact...