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» Semi-supervised Discriminant Analysis
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BMCBI
2010
132views more  BMCBI 2010»
13 years 9 months ago
Error margin analysis for feature gene extraction
Background: Feature gene extraction is a fundamental issue in microarray-based biomarker discovery. It is normally treated as an optimization problem of finding the best predictiv...
Chi Kin Chow, Hai Long Zhu, Jessica Lacy, Winston ...
FGR
2008
IEEE
153views Biometrics» more  FGR 2008»
14 years 3 months ago
Facial image analysis using local feature adaptation prior to learning
Many facial image analysis methods rely on learningbased techniques such as Adaboost or SVMs to project classifiers based on the selection of local image filters (e.g., Haar and...
Rogerio Feris, Ying-li Tian, Yun Zhai, Arun Hampap...
BMCBI
2008
173views more  BMCBI 2008»
13 years 9 months ago
Improved machine learning method for analysis of gas phase chemistry of peptides
Background: Accurate peptide identification is important to high-throughput proteomics analyses that use mass spectrometry. Search programs compare fragmentation spectra (MS/MS) o...
Allison Gehrke, Shaojun Sun, Lukasz A. Kurgan, Nat...

Book
640views
15 years 7 months ago
Introduction to Pattern Recognition
"Pattern recognition techniques are concerned with the theory and algorithms of putting abstract objects, e.g., measurements made on physical objects, into categories. Typical...
Sargur Srihari
CVPR
2004
IEEE
14 years 11 months ago
Random Sampling LDA for Face Recognition
Linear Discriminant Analysis (LDA) is a popular feature extraction technique for face recognition. However, It often suffers from the small sample size problem when dealing with t...
Xiaogang Wang, Xiaoou Tang