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» Learning of Boolean Functions Using Support Vector Machines
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GECCO
2006
Springer
140views Optimization» more  GECCO 2006»
14 years 24 days ago
A representational ecology for learning classifier systems
The representation used by a learning algorithm introduces a bias which is more or less well-suited to any given learning problem. It is well known that, across all possible probl...
James A. R. Marshall, Tim Kovacs
IGARSS
2010
13 years 4 months ago
Calibrating probabilities for hyperspectral classification of rock types
This paper investigates the performance of machine learning methods for classifying rock types from hyperspectral data. The main objective is to test the impact on classification ...
Sildomar T. Monteiro, Richard J. Murphy
MLDM
2005
Springer
14 years 2 months ago
An Automatic Face Recognition System in the Near Infrared Spectrum
Face recognition is a challenging visual classification task, especially when the lighting conditions can not be controlled. In this paper, we present an automatic face recognitio...
Shuyan Zhao, Rolf-Rainer Grigat
ECML
2006
Springer
14 years 25 days ago
Evaluating Feature Selection for SVMs in High Dimensions
We perform a systematic evaluation of feature selection (FS) methods for support vector machines (SVMs) using simulated high-dimensional data (up to 5000 dimensions). Several findi...
Roland Nilsson, José M. Peña, Johan ...
ICIP
2008
IEEE
14 years 3 months ago
Blind image steganalysis based on run-length histogram analysis
In this paper, a new, simple but effective method is proposed for blind image steganalysis, which is based on run-length histogram analysis. Higher-order statistics of characteris...
Jing Dong, Tieniu Tan