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NECO
1998
119views more  NECO 1998»
15 years 6 months ago
Density Estimation by Mixture Models with Smoothing Priors
In the statistical approach for self-organizing maps (SOMs), learning is regarded as an estimation algorithm for a Gaussian mixture model with a Gaussian smoothing prior on the ce...
Akio Utsugi
JMLR
2006
145views more  JMLR 2006»
15 years 6 months ago
Ensemble Pruning Via Semi-definite Programming
An ensemble is a group of learning models that jointly solve a problem. However, the ensembles generated by existing techniques are sometimes unnecessarily large, which can lead t...
Yi Zhang 0006, Samuel Burer, W. Nick Street
CVPR
2007
IEEE
16 years 8 months ago
OPTIMOL: automatic Online Picture collecTion via Incremental MOdel Learning
A well-built dataset is a necessary starting point for advanced computer vision research. It plays a crucial role in evaluation and provides a continuous challenge to stateof-the-...
Li-Jia Li, Gang Wang, Fei-Fei Li 0002
SEW
2003
IEEE
15 years 12 months ago
Model-Based Software Testing via Incremental Treatment Learning
Model-based software has become quite popular in recent years, making its way into a broad range of areas, including the aerospace industry. The models provide an easy graphical i...
Dustin Geletko, Tim Menzies
ISNN
2005
Springer
16 years 3 days ago
Multiple Parameter Selection for LS-SVM Using Smooth Leave-One-Out Error
In least squares support vector (LS-SVM), the key challenge lies in the selection of free parameters such as kernel parameters and tradeoff parameter. However, when a large number ...
Liefeng Bo, Ling Wang, Licheng Jiao