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» Feature Subset Selection Using a Genetic Algorithm
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ICML
2000
IEEE
16 years 7 months ago
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
Algorithms for feature selection fall into two broad categories: wrappers that use the learning algorithm itself to evaluate the usefulness of features and filters that evaluate f...
Mark A. Hall
JMLR
2008
83views more  JMLR 2008»
15 years 6 months ago
Generalization from Observed to Unobserved Features by Clustering
We argue that when objects are characterized by many attributes, clustering them on the basis of a random subset of these attributes can capture information on the unobserved attr...
Eyal Krupka, Naftali Tishby
ICRA
2009
IEEE
165views Robotics» more  ICRA 2009»
16 years 29 days ago
Robust servo-control for underwater robots using banks of visual filters
—We present an application of machine learning to the semi-automatic synthesis of robust servo-trackers for underwater robotics. In particular, we investigate an approach based o...
Junaed Sattar, Gregory Dudek
GECCO
2004
Springer
104views Optimization» more  GECCO 2004»
15 years 11 months ago
Optimization of Constructive Solid Geometry Via a Tree-Based Multi-objective Genetic Algorithm
This paper presents the multi-objective evolutionary optimization of three-dimensional geometry represented via constructive solid geometry (CSG), a binary tree of boolean operatio...
Karim Hamza, Kazuhiro Saitou
ICML
2004
IEEE
15 years 11 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