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BMCBI
2004
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8 years 9 months ago
Oligo kernels for datamining on biological sequences: a case study on prokaryotic translation initiation sites
Background: Kernel-based learning algorithms are among the most advanced machine learning methods and have been successfully applied to a variety of sequence classification tasks ...
Peter Meinicke, Maike Tech, Burkhard Morgenstern, ...
NIPS
1990
8 years 10 months ago
Convergence of a Neural Network Classifier
In this paper, we show that the LVQ learning algorithm converges to locally asymptotic stable equilibria of an ordinary differential equation. We show that the learning algorithm ...
John S. Baras, Anthony LaVigna
FLAIRS
1998
8 years 10 months ago
DFA Learning of Opponent Strategies
This work studies the control of robots in the adversarial world of "Hunt the Wumpus". The hybrid learning algorithm which controls the robots behavior is a combination ...
Gilbert L. Peterson, Diane J. Cook
UAI
2003
8 years 10 months ago
Large-Sample Learning of Bayesian Networks is NP-Hard
In this paper, we provide new complexity results for algorithms that learn discrete-variable Bayesian networks from data. Our results apply whenever the learning algorithm uses a ...
David Maxwell Chickering, Christopher Meek, David ...
ACL
1998
8 years 10 months ago
Learning Optimal Dialogue Strategies: A Case Study of a Spoken Dialogue Agent for Email
This paper describes a novel method by which a dialogue agent can learn to choose an optimal dialogue strategy. While it is widely agreed that dialogue strategies should be formul...
Marilyn A. Walker, Jeanne Frommer, Shrikanth Naray...
NIPS
2003
8 years 10 months ago
Large Scale Online Learning
We consider situations where training data is abundant and computing resources are comparatively scarce. We argue that suitably designed online learning algorithms asymptotically ...
Léon Bottou, Yann LeCun
NIPS
2001
8 years 10 months ago
The Steering Approach for Multi-Criteria Reinforcement Learning
We consider the problem of learning to attain multiple goals in a dynamic environment, which is initially unknown. In addition, the environment may contain arbitrarily varying ele...
Shie Mannor, Nahum Shimkin
NIPS
2001
8 years 10 months ago
Algorithmic Luckiness
Classical statistical learning theory studies the generalisation performance of machine learning algorithms rather indirectly. One of the main detours is that algorithms are studi...
Ralf Herbrich, Robert C. Williamson
NIPS
2004
8 years 10 months ago
Stable adaptive control with online learning
Learning algorithms have enjoyed numerous successes in robotic control tasks. In problems with time-varying dynamics, online learning methods have also proved to be a powerful too...
Andrew Y. Ng, H. Jin Kim
NIPS
2004
8 years 10 months ago
Convergence and No-Regret in Multiagent Learning
Learning in a multiagent system is a challenging problem due to two key factors. First, if other agents are simultaneously learning then the environment is no longer stationary, t...
Michael H. Bowling
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