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ALT
2002
Springer
16 years 3 months ago
Optimally-Smooth Adaptive Boosting and Application to Agnostic Learning
We describe a new boosting algorithm that is the first such algorithm to be both smooth and adaptive. These two features make possible performance improvements for many learning ...
Dmitry Gavinsky
CIRA
2007
IEEE
148views Robotics» more  CIRA 2007»
16 years 27 days ago
Reinforcement Learning with a Supervisor for a Mobile Robot in a Real-world Environment
– This paper describes two experiments with supervised reinforcement learning (RL) on a real, mobile robot. Two types of experiments were preformed. One tests the robot’s relia...
Karla Conn, Richard Alan Peters II
AIED
2007
Springer
16 years 22 days ago
Dialog Convergence and Learning
Abstract. In this paper we examine whether the student-to-tutor convergence of lexical and speech features is a useful predictor of learning in a corpus of spoken tutorial dialogs....
Arthur Ward, Diane J. Litman
ATAL
2007
Springer
16 years 22 days ago
Reducing the complexity of multiagent reinforcement learning
It is known that the complexity of the reinforcement learning algorithms, such as Q-learning, may be exponential in the number of environment’s states. It was shown, however, th...
Andriy Burkov, Brahim Chaib-draa
ICTAI
2006
IEEE
16 years 17 days ago
MI-Winnow: A New Multiple-Instance Learning Algorithm
We present MI-Winnow, a new multiple-instance learning (MIL) algorithm that provides a new technique to convert MIL data into standard supervised data. In MIL each example is a co...
Sharath R. Cholleti, Sally A. Goldman, Rouhollah R...