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» Superset Learning Based on Generalized Loss Minimization
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GECCO
2005
Springer
155views Optimization» more  GECCO 2005»
15 years 3 months ago
A pareto archive evolutionary strategy based radial basis function neural network training algorithm for failure rate prediction
This paper outlines a radial basis function neural network approach to predict the failures in overhead distribution lines of power delivery systems. The RBF networks are trained ...
Grant Cochenour, Jerad Simon, Sanjoy Das, Anil Pah...
WWW
2007
ACM
15 years 10 months ago
Reliable QoS monitoring based on client feedback
Service-level agreements (SLAs) establish a contract between service providers and clients concerning Quality of Service (QoS) parameters. Without proper penalties, service provid...
Radu Jurca, Boi Faltings, Walter Binder
AAAI
2007
15 years 12 days ago
Temporal Difference and Policy Search Methods for Reinforcement Learning: An Empirical Comparison
Reinforcement learning (RL) methods have become popular in recent years because of their ability to solve complex tasks with minimal feedback. Both genetic algorithms (GAs) and te...
Matthew E. Taylor, Shimon Whiteson, Peter Stone
NIPS
2007
14 years 11 months ago
A Spectral Regularization Framework for Multi-Task Structure Learning
Learning the common structure shared by a set of supervised tasks is an important practical and theoretical problem. Knowledge of this structure may lead to better generalization ...
Andreas Argyriou, Charles A. Micchelli, Massimilia...
COLT
2005
Springer
15 years 3 months ago
Margin-Based Ranking Meets Boosting in the Middle
Abstract. We present several results related to ranking. We give a general margin-based bound for ranking based on the L∞ covering number of the hypothesis space. Our bound sugge...
Cynthia Rudin, Corinna Cortes, Mehryar Mohri, Robe...