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AAAI
2007
15 years 9 months ago
Active Imitation Learning
Imitation learning, also called learning by watching or programming by demonstration, has emerged as a means of accelerating many reinforcement learning tasks. Previous work has s...
Aaron P. Shon, Deepak Verma, Rajesh P. N. Rao
NIPS
1998
15 years 8 months ago
Gradient Descent for General Reinforcement Learning
A simple learning rule is derived, the VAPS algorithm, which can be instantiated to generate a wide range of new reinforcementlearning algorithms. These algorithms solve a number ...
Leemon C. Baird III, Andrew W. Moore
TLT
2010
90views more  TLT 2010»
15 years 5 months ago
To Flow and Not to Freeze: Applying Flow Experience to Mobile Learning
— A key design goal of mobile learning is that its built-in experiences are enjoyable and proactive, empowering the learner with the knowledge and ability to self-manage. This im...
Jungho Park, David Parsons, Hokyoung Ryu
KCAP
2009
ACM
16 years 1 months ago
Reducing class imbalance during active learning for named entity annotation
In lots of natural language processing tasks, the classes to be dealt with often occur heavily imbalanced in the underlying data set and classifiers trained on such skewed data t...
Katrin Tomanek, Udo Hahn
ACL
2008
15 years 8 months ago
Learning Bigrams from Unigrams
Traditional wisdom holds that once documents are turned into bag-of-words (unigram count) vectors, word orders are completely lost. We introduce an approach that, perhaps surprisi...
Xiaojin Zhu, Andrew B. Goldberg, Michael Rabbat, R...