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» Sublinear Optimization for Machine Learning
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124
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ALT
2007
Springer
15 years 8 months ago
On Universal Transfer Learning
In transfer learning the aim is to solve new learning tasks using fewer examples by using information gained from solving related tasks. Existing transfer learning methods have be...
M. M. Hassan Mahmud
SDM
2012
SIAM
216views Data Mining» more  SDM 2012»
13 years 4 months ago
Feature Selection "Tomography" - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable
:  Feature Selection “Tomography” - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable George Forman HP Laboratories HPL-2010-19R1 Feature selection; ...
George Forman
93
Voted
IJCNN
2006
IEEE
15 years 8 months ago
Learning to Rank by Maximizing AUC with Linear Programming
— Area Under the ROC Curve (AUC) is often used to evaluate ranking performance in binary classification problems. Several researchers have approached AUC optimization by approxi...
Kaan Ataman, W. Nick Street, Yi Zhang
84
Voted
ICRA
2010
IEEE
149views Robotics» more  ICRA 2010»
15 years 24 days ago
A simple learning strategy for high-speed quadrocopter multi-flips
— We describe a simple and intuitive policy gradient method for improving parametrized quadrocopter multi-flips by combining iterative experiments with information from a first...
Sergei Lupashin, Angela Schöllig, Michael She...
GECCO
2006
Springer
208views Optimization» more  GECCO 2006»
15 years 6 months ago
Comparing evolutionary and temporal difference methods in a reinforcement learning domain
Both genetic algorithms (GAs) and temporal difference (TD) methods have proven effective at solving reinforcement learning (RL) problems. However, since few rigorous empirical com...
Matthew E. Taylor, Shimon Whiteson, Peter Stone