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» Aerial Lidar Data Classification using AdaBoost
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KDD
2006
ACM
170views Data Mining» more  KDD 2006»
14 years 5 months ago
Computer aided detection via asymmetric cascade of sparse hyperplane classifiers
This paper describes a novel classification method for computer aided detection (CAD) that identifies structures of interest from medical images. CAD problems are challenging larg...
Jinbo Bi, Senthil Periaswamy, Kazunori Okada, Tosh...
TMM
2010
241views Management» more  TMM 2010»
12 years 11 months ago
Mining Compositional Features From GPS and Visual Cues for Event Recognition in Photo Collections
As digital cameras with Global Positioning System (GPS) capability become available and people geotag their photos using other means, it is of great interest to annotate semantic e...
Junsong Yuan, Jiebo Luo, Ying Wu
PAMI
2007
196views more  PAMI 2007»
13 years 4 months ago
Bayesian Analysis of Lidar Signals with Multiple Returns
—Time-Correlated Single Photon Counting and Burst Illumination Laser data can be used for range profiling and target classification. In general, the problem is to analyze the res...
Sergio Hernandez-Marin, Andrew M. Wallace, Gavin J...
IJSI
2008
156views more  IJSI 2008»
13 years 5 months ago
Co-Training by Committee: A Generalized Framework for Semi-Supervised Learning with Committees
Many data mining applications have a large amount of data but labeling data is often difficult, expensive, or time consuming, as it requires human experts for annotation. Semi-supe...
Mohamed Farouk Abdel Hady, Friedhelm Schwenker
ESANN
2006
13 years 6 months ago
Random Forests Feature Selection with K-PLS: Detecting Ischemia from Magnetocardiograms
Random Forests were introduced by Breiman for feature (variable) selection and improved predictions for decision tree models. The resulting model is often superior to AdaBoost and ...
Long Han, Mark J. Embrechts, Boleslaw K. Szymanski...