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ICML
2004
IEEE
15 years 5 months ago
Gradient LASSO for feature selection
LASSO (Least Absolute Shrinkage and Selection Operator) is a useful tool to achieve the shrinkage and variable selection simultaneously. Since LASSO uses the L1 penalty, the optim...
Yongdai Kim, Jinseog Kim
AH
2000
Springer
15 years 4 months ago
A Connectionist Approach for Supporting Personalized Learning in a Web-Based Learning Environment
The paper investigates the use of computational intelligence for adaptive lesson presentation in a Web-based learning environment. A specialized connectionist architecture is devel...
Kyparisia A. Papanikolaou, George D. Magoulas, Mar...
ICML
2006
IEEE
16 years 19 days ago
An intrinsic reward mechanism for efficient exploration
How should a reinforcement learning agent act if its sole purpose is to efficiently learn an optimal policy for later use? In other words, how should it explore, to be able to exp...
Özgür Simsek, Andrew G. Barto
HAIS
2010
Springer
14 years 9 months ago
Reducing Dimensionality in Multiple Instance Learning with a Filter Method
In this article, we describe a feature selection algorithm which can automatically find relevant features for multiple instance learning. Multiple instance learning is considered a...
Amelia Zafra, Mykola Pechenizkiy, Sebastián...
CGO
2010
IEEE
15 years 6 months ago
Automatic creation of tile size selection models
Tiling is a widely used loop transformation for exposing/exploiting parallelism and data locality. Effective use of tiling requires selection and tuning of the tile sizes. This is...
Tomofumi Yuki, Lakshminarayanan Renganarayanan, Sa...