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EMNLP
2008
13 years 9 months ago
Online Methods for Multi-Domain Learning and Adaptation
NLP tasks are often domain specific, yet systems can learn behaviors across multiple domains. We develop a new multi-domain online learning framework based on parameter combinatio...
Mark Dredze, Koby Crammer
ICPR
2008
IEEE
14 years 8 months ago
Exploiting qualitative domain knowledge for learning Bayesian network parameters with incomplete data
When a large amount of data are missing, or when multiple hidden nodes exist, learning parameters in Bayesian networks (BNs) becomes extremely difficult. This paper presents a lea...
Qiang Ji, Wenhui Liao
ICRA
2008
IEEE
169views Robotics» more  ICRA 2008»
14 years 1 months ago
Sparse incremental learning for interactive robot control policy estimation
— We are interested in transferring control policies for arbitrary tasks from a human to a robot. Using interactive demonstration via teloperation as our transfer scenario, we ca...
Daniel H. Grollman, Odest Chadwicke Jenkins
AIPS
2004
13 years 8 months ago
Learning Domain-Specific Control Knowledge from Random Walks
We describe and evaluate a system for learning domainspecific control knowledge. In particular, given a planning domain, the goal is to output a control policy that performs well ...
Alan Fern, Sung Wook Yoon, Robert Givan
ML
2010
ACM
135views Machine Learning» more  ML 2010»
13 years 2 months ago
Multi-domain learning by confidence-weighted parameter combination
State-of-the-art statistical NLP systems for a variety of tasks learn from labeled training data that is often domain specific. However, there may be multiple domains or sources o...
Mark Dredze, Alex Kulesza, Koby Crammer