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» Objective reduction using a feature selection technique
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CVPR
2005
IEEE
14 years 11 months ago
Object Class Recognition Using Multiple Layer Boosting with Heterogeneous Features
We combine local texture features (PCA-SIFT), global features (shape context), and spatial features within a single multi-layer AdaBoost model of object class recognition. The fir...
Wei Zhang 0002, Bing Yu, Gregory J. Zelinsky, Dimi...
CSDA
2006
138views more  CSDA 2006»
13 years 9 months ago
Automatic dimensionality selection from the scree plot via the use of profile likelihood
Most dimension reduction techniques produce ordered coordinates so that only the first few coordinates need be considered in subsequent analyses. The choice of how many coordinate...
Mu Zhu, Ali Ghodsi
IEEEICCI
2006
IEEE
14 years 3 months ago
Using Feature Selection Filtering Methods for Binding Site Predictions
Currently the best algorithms for transcription factor binding site prediction are severely limited in accuracy. In previous work we applied classification techniques on predictio...
Yi Sun, Mark Robinson, Rod Adams, Rene te Boekhors...
BMCBI
2006
140views more  BMCBI 2006»
13 years 9 months ago
Feature selection using Haar wavelet power spectrum
Background: Feature selection is an approach to overcome the 'curse of dimensionality' in complex researches like disease classification using microarrays. Statistical m...
Prabakaran Subramani, Rajendra Sahu, Shekhar Verma
PRL
2010
159views more  PRL 2010»
13 years 7 months ago
Creating diverse nearest-neighbour ensembles using simultaneous metaheuristic feature selection
The nearest-neighbour (1NN) classifier has long been used in pattern recognition, exploratory data analysis, and data mining problems. A vital consideration in obtaining good res...
Muhammad Atif Tahir, Jim E. Smith