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» High-Level Feature Extraction Experiments for TRECVID 2007
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BMCBI
2004
205views more  BMCBI 2004»
13 years 8 months ago
A combinational feature selection and ensemble neural network method for classification of gene expression data
Background: Microarray experiments are becoming a powerful tool for clinical diagnosis, as they have the potential to discover gene expression patterns that are characteristic for...
Bing Liu, Qinghua Cui, Tianzi Jiang, Songde Ma
BMCBI
2004
208views more  BMCBI 2004»
13 years 8 months ago
Hybrid clustering for microarray image analysis combining intensity and shape features
Background: Image analysis is the first crucial step to obtain reliable results from microarray experiments. First, areas in the image belonging to single spots have to be identif...
Jörg Rahnenführer, Daniel Bozinov
PAMI
2007
157views more  PAMI 2007»
13 years 8 months ago
Pores and Ridges: High-Resolution Fingerprint Matching Using Level 3 Features
—Fingerprint friction ridge details are generally described in a hierarchical order at three different levels, namely, Level 1 (pattern), Level 2 (minutia points), and Level 3 (p...
Anil K. Jain, Yi Chen, Meltem Demirkus
ICDAR
2007
IEEE
14 years 11 days ago
On the Use of Lexeme Features for Writer Verification
Document examiners use a variety of features to analyze a given handwritten document for writer verification. The challenge in the automatic classification of a pair of documents ...
A. Bhardwaj, A. Singh, Harish Srinivasan, Sargur N...
ICCBR
2007
Springer
14 years 2 months ago
Knowledge Extraction and Summarization for an Application of Textual Case-Based Interpretation
Abstract. This paper presents KES (Knowledge Extraction and Summarization), a new knowledge-enhanced approach that builds a case memory out of episodic textual narratives. These na...
Eni Mustafaraj, Martin Hoof, Bernd Freisleben