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» Evaluating learning algorithms and classifiers
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ICML
2010
IEEE
15 years 5 months ago
Restricted Boltzmann Machines are Hard to Approximately Evaluate or Simulate
Restricted Boltzmann Machines (RBMs) are a type of probability model over the Boolean cube {-1, 1}n that have recently received much attention. We establish the intractability of ...
Philip M. Long, Rocco A. Servedio
COR
2006
97views more  COR 2006»
15 years 4 months ago
Evaluating the performance of cost-based discretization versus entropy- and error-based discretization
Discretization is defined as the process that divides continuous numeric values into intervals of discrete categorical values. In this article, the concept of cost-based discretiz...
Davy Janssens, Tom Brijs, Koen Vanhoof, Geert Wets
ICML
2004
IEEE
16 years 5 months ago
Sequential skewing: an improved skewing algorithm
This paper extends previous work on the Skewing algorithm, a promising approach that allows greedy decision tree induction algorithms to handle problematic functions such as parit...
Soumya Ray, David Page
NCA
2002
IEEE
15 years 4 months ago
Comparison of Algorithmic and Machine Learning Approaches for the Automatic Fitting of Gaussian Peaks
Fitting gaussian peaks to experimental data is important in many disciplines, including nuclear spectroscopy. Nonlinear least squares fitting methods have been in use for a long t...
Radwan E. Abdel-Aal
ICPR
2010
IEEE
15 years 2 months ago
Classification of Polarimetric SAR Images Using Evolutionary RBF Networks
This paper proposes an evolutionary RBF network classifier for polarimetric synthetic aperture radar ( SAR) images. The proposed feature extraction process utilizes the full covar...
Ince Turker, Serkan Kiranyaz, Moncef Gabbouj