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» On the Complexity of Function Learning
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ICML
2008
IEEE
16 years 4 months ago
The asymptotics of semi-supervised learning in discriminative probabilistic models
Semi-supervised learning aims at taking advantage of unlabeled data to improve the efficiency of supervised learning procedures. For discriminative models however, this is a chall...
François Yvon, Nataliya Sokolovska, Olivier...
ESANN
2006
15 years 4 months ago
A multiagent architecture for concurrent reinforcement learning
In this paper we propose a multiagent architecture for implementing concurrent reinforcement learning, an approach where several agents, sharing the same environment, perceptions ...
Victor Uc Cetina
IJON
2007
131views more  IJON 2007»
15 years 3 months ago
Margin-based active learning for LVQ networks
In this article, we extend a local prototype-based learning model by active learning, which gives the learner the capability to select training samples and thereby increase speed a...
Frank-Michael Schleif, Barbara Hammer, Thomas Vill...
ICDE
2008
IEEE
189views Database» more  ICDE 2008»
15 years 9 months ago
Adapting ranking functions to user preference
— Learning to rank has become a popular method for web search ranking. Traditionally, expert-judged examples are the major training resource for machine learned web ranking, whic...
Keke Chen, Ya Zhang, Zhaohui Zheng, Hongyuan Zha, ...
ICML
2006
IEEE
16 years 4 months ago
A graphical model for predicting protein molecular function
We present a simple statistical model of molecular function evolution to predict protein function. The model description encodes general knowledge of how molecular function evolve...
Barbara E. Engelhardt, Michael I. Jordan, Steven E...