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ISNN
2004
Springer
14 years 1 months ago
Progressive Principal Component Analysis
Abstract. Principal Component Analysis (PCA) is a feature extraction approach directly based on a whole vector pattern and acquires a set of projections that can realize the best r...
Jun Liu, Songcan Chen, Zhi-Hua Zhou
ICASSP
2008
IEEE
14 years 2 months ago
System combination using auxiliary information for speaker verification
Recent studies in speaker recognition have shown that scorelevel combination of subsystems can yield significant performance gains over individual subsystems. We explore the use ...
Luciana Ferrer, Martin Graciarena, Argyrios Zymnis...
IJCNN
2008
IEEE
14 years 2 months ago
Feature selection based on kernel discriminant analysis for multi-class problems
— We propose a feature selection criterion based on kernel discriminant analysis (KDA) for an -class problem, which finds eigenvectors on which the projected class data are loca...
Tsuneyoshi Ishii, Shigeo Abe
ICONIP
2007
13 years 9 months ago
Using Generalization Error Bounds to Train the Set Covering Machine
In this paper we eliminate the need for parameter estimation associated with the set covering machine (SCM) by directly minimizing generalization error bounds. Firstly, we consider...
Zakria Hussain, John Shawe-Taylor
BMCBI
2010
208views more  BMCBI 2010»
13 years 8 months ago
Using machine learning to speed up manual image annotation: application to a 3D imaging protocol for measuring single cell gene
Background: Image analysis is an essential component in many biological experiments that study gene expression, cell cycle progression, and protein localization. A protocol for tr...
Zafer Aydin, John I. Murray, Robert H. Waterston, ...