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EMNLP
2007
13 years 9 months ago
Syntactic Re-Alignment Models for Machine Translation
We present a method for improving word alignment for statistical syntax-based machine translation that employs a syntactically informed alignment model closer to the translation m...
Jonathan May, Kevin Knight
COGSR
2011
105views more  COGSR 2011»
13 years 2 months ago
Inductive rule learning on the knowledge level
We present an application of the analytical inductive programming system Igor to learning sets of recursive rules from positive experience. We propose that this approach can be us...
Ute Schmid, Emanuel Kitzelmann
ICRA
2006
IEEE
87views Robotics» more  ICRA 2006»
14 years 1 months ago
Learning to Predict Slip for Ground Robots
— In this paper we predict the amount of slip an exploration rover would experience using stereo imagery by learning from previous examples of traversing similar terrain. To do t...
Anelia Angelova, Larry Matthies, Daniel M. Helmick...
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ICIA
2007
13 years 9 months ago
Modeling Human-Agent Interaction with Active Ontologies
As computer systems continue to grow in power and access more networked content and services, we believe there will be an increasing need to provide more user-centric systems that...
Didier Guzzoni, Charles Baur, Adam Cheyer
IEEEVAST
2010
13 years 2 months ago
A visual analytics approach to model learning
The process of learning models from raw data typically requires a substantial amount of user input during the model initialization phase. We present an assistive visualization sys...
Supriya Garg, I. V. Ramakrishnan, Klaus Mueller