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» Variable selection using neural-network models
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ICML
2004
IEEE
15 years 3 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
KDD
1998
ACM
442views Data Mining» more  KDD 1998»
15 years 2 months ago
BAYDA: Software for Bayesian Classification and Feature Selection
BAYDA is a software package for flexible data analysis in predictive data mining tasks. The mathematical model underlying the program is based on a simple Bayesian network, the Na...
Petri Kontkanen, Petri Myllymäki, Tomi Siland...
OOPSLA
2005
Springer
15 years 3 months ago
Modeling architectural patterns using architectural primitives
Architectural patterns are a key point in architectural documentation. Regrettably, there is poor support for modeling architectural patterns, because the pattern elements are not...
Uwe Zdun, Paris Avgeriou
CADE
2000
Springer
15 years 2 months ago
Two Techniques to Improve Finite Model Search
Abstract. This article introduces two techniques to improve the propagation efficiency of CSP based finite model generation methods. One approach consists in statically rewriting ...
Gilles Audemard, Belaid Benhamou, Laurent Henocque
ESANN
2006
14 years 11 months ago
Dynamical reservoir properties as network effects
It has been proposed that chaos can serve as a reservoir providing an infinite number of dynamical states [1, 2, 3, 4, 5]. These can be interpreted as different behaviors, search a...
Carlos Lourenço