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» Parameterized Learning Complexity
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ICANN
2009
Springer
15 years 11 months ago
Constrained Learning Vector Quantization or Relaxed k-Separability
Neural networks and other sophisticated machine learning algorithms frequently miss simple solutions that can be discovered by a more constrained learning methods. Transition from ...
Marek Grochowski, Wlodzislaw Duch
IUI
1999
ACM
15 years 10 months ago
Programming by Demonstration: An Inductive Learning Formulation
Although Programming by Demonstration (PBD) has the potential to improve the productivity of unsophisticated users, previous PBD systems have used brittle, heuristic, domain-speci...
Tessa A. Lau, Daniel S. Weld
199
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DAGM
2008
Springer
15 years 8 months ago
Approximate Parameter Learning in Conditional Random Fields: An Empirical Investigation
We investigate maximum likelihood parameter learning in Conditional Random Fields (CRF) and present an empirical study of pseudo-likelihood (PL) based approximations of the paramet...
Filip Korc, Wolfgang Förstner
IJCAI
2007
15 years 7 months ago
Learning to Count by Think Aloud Imitation
Although necessary, learning to discover new solutions is often long and difficult, even for supposedly simple tasks such as counting. On the other hand, learning by imitation pr...
Laurent Orseau
IJCAI
2007
15 years 7 months ago
Analogical Learning in a Turn-Based Strategy Game
A key problem in playing strategy games is learning how to allocate resources effectively. This can be a difficult task for machine learning when the connections between actions a...
Thomas R. Hinrichs, Kenneth D. Forbus