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BMCBI
2010
132views more  BMCBI 2010»
13 years 7 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 ...
ISM
2008
IEEE
140views Multimedia» more  ISM 2008»
14 years 2 months ago
Medical Video Event Classification Using Shared Features
Advances in video technology are being incorporated into today’s medical research and education. Medical videos contain important medical events, such as diagnostic or therapeut...
Yu Cao, Shih-Hsi Liu, Ming Li, Sung Baang, Sanqing...
TMI
2010
172views more  TMI 2010»
13 years 6 months ago
Comparison of AdaBoost and Support Vector Machines for Detecting Alzheimer's Disease Through Automated Hippocampal Segmentation
Abstract— We compared four automated methods for hippocampal segmentation using different machine learning algorithms (1) hierarchical AdaBoost, (2) Support Vector Machines (SVM)...
Jonathan H. Morra, Zhuowen Tu, Liana G. Apostolova...
SAC
2006
ACM
14 years 1 months ago
The impact of sample reduction on PCA-based feature extraction for supervised learning
“The curse of dimensionality” is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity and classification error in high dimension...
Mykola Pechenizkiy, Seppo Puuronen, Alexey Tsymbal
IPMU
2010
Springer
13 years 6 months ago
Attribute Value Selection Considering the Minimum Description Length Approach and Feature Granularity
Abstract. In this paper we introduce a new approach to automatic attribute and granularity selection for building optimum regression trees. The method is based on the minimum descr...
Kemal Ince, Frank Klawonn