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BMCBI
2011
13 years 2 months ago
Phenotype Recognition with Combined Features and Random Subspace Classifier Ensemble
Background: Automated, image based high-content screening is a fundamental tool for discovery in biological science. Modern robotic fluorescence microscopes are able to capture th...
Bailing Zhang, Tuan D. Pham
ICMCS
2005
IEEE
110views Multimedia» more  ICMCS 2005»
14 years 1 months ago
Combining Caption and Visual Features for Semantic Event Classification of Baseball Video
In baseball game, an event is defined as the portion of video clip between two pitches, and a play is defined as a batter finishing his plate appearance. A play is a concatenation...
Wen-Nung Lie, Sheng-Hsiung Shia
3DIM
2007
IEEE
13 years 11 months ago
Aerial Lidar Data Classification using AdaBoost
We use the AdaBoost algorithm to classify 3D aerial lidar scattered height data into four categories: road, grass, buildings, and trees. To do so we use five features: height, hei...
Suresh K. Lodha, Darren N. Fitzpatrick, David P. H...
ISBI
2004
IEEE
14 years 8 months ago
Automated Classification of Subcellular Patterns In Multicell Images Without Segmentation Into Single Cells
Fluorescence microscope images capture information from an entire field of view, which often comprises several cells scattered on the slide. We have previously trained classifiers...
Kai Huang, Robert F. Murphy
SDM
2012
SIAM
216views Data Mining» more  SDM 2012»
11 years 10 months ago
Feature Selection "Tomography" - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable
:  Feature Selection “Tomography” - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable George Forman HP Laboratories HPL-2010-19R1 Feature selection; ...
George Forman