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» Feature Subset Selection Using a Genetic Algorithm
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ICML
2000
IEEE
14 years 9 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»
13 years 9 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»
14 years 3 months 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»
14 years 2 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
14 years 2 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