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SDM
2012
SIAM
237views Data Mining» more  SDM 2012»
13 years 4 months ago
A Distributed Kernel Summation Framework for General-Dimension Machine Learning
Kernel summations are a ubiquitous key computational bottleneck in many data analysis methods. In this paper, we attempt to marry, for the first time, the best relevant technique...
Dongryeol Lee, Richard W. Vuduc, Alexander G. Gray
91
Voted
ACL
2008
15 years 3 months ago
Name Translation in Statistical Machine Translation - Learning When to Transliterate
We present a method to transliterate names in the framework of end-to-end statistical machine translation. The system is trained to learn when to transliterate. For Arabic to Engl...
Ulf Hermjakob, Kevin Knight, Hal Daumé III
115
Voted
BMCBI
2008
123views more  BMCBI 2008»
15 years 2 months ago
Pol II promoter prediction using characteristic 4-mer motifs: a machine learning approach
Background: Eukaryotic promoter prediction using computational analysis techniques is one of the most difficult jobs in computational genomics that is essential for constructing a...
Firoz Anwar, Syed Murtuza Baker, Taskeed Jabid, Md...
CVPR
2008
IEEE
16 years 4 months ago
Learning for stereo vision using the structured support vector machine
We present a random field based model for stereo vision with explicit occlusion labeling in a probabilistic framework. The model employs non-parametric cost functions that can be ...
Yunpeng Li, Daniel P. Huttenlocher
111
Voted
ACL
2007
15 years 3 months ago
A Seed-driven Bottom-up Machine Learning Framework for Extracting Relations of Various Complexity
A minimally supervised machine learning framework is described for extracting relations of various complexity. Bootstrapping starts from a small set of n-ary relation instances as...
Feiyu Xu, Hans Uszkoreit, Hong Li