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feature slection
  • Classification:Database system - Other database
  • Development Tool:matlab
  • Sise:37.91 MB
  • Upload time:2019/2/3 11:12:04
  • Uploader:zhidal
  • Download Statistics:
Description
1504_Lab1.project 1504_Lab1.project 1504_Lab1.project 1504_Lab1.project




File list:
FEAST-master
...........\.gitignore
...........\BetaGamma.c
...........\CMIM.c
...........\CompileFEAST.m
...........\CondMI.c
...........\DISR.c
...........\FCBF.m
...........\feast.bib
...........\feast.m
...........\FSAlgorithms.h
...........\FSToolbox.h
...........\FSToolboxMex.c
...........\ICAP.c
...........\JMI.c
...........\LICENSE
...........\Makefile
...........\MIM.c
...........\MIM.m
...........\mRMR_D.c
...........\README.md
...........\RELIEF.m
...........\testFSToolbox.m
Related source code
[Image-Zernike-moments-shape-feature-...] - Zernike moment paper
[feature_extraction] - code for feature extraction
[GTF_Features] - auditry feture xtrction
[GMM_multiple_features_image_segmenta...] - this gives you implementation of growcut algorithm
[featuresvoice-unvoice] - features to voice and un voice classification , pitch determination whith AMDF and other features like Spectral Centroid and Entropy of the energy
[texture-feature-extraction] - Texture detection using GLCM. GLCM describes four properties known as contrast, correlation, energy and homogeneity.
[audioFeatureExtraction] - A program used for feature extraction in matlab. Can be used for emotion recognition in artificial neural network.
[feature_extraction_project] - Feature Extraction Code
[ColorFeatures] - TO CALCULATE COLOUR FEATURE
[A-GA-based-feature-selection-and-par...] - Support Vector Machines, one of the new techniques for pattern classifi cation, have been widely used in many application areas. The kernelparameters setting for SVM in a training process impacts on the classifi cation accuracy. Feature selection is another factor that impactsclassifi cation accuracy. The objective of this research is to simultaneously optimize the parameters and feature subset without degrading the SVMclassifi cation accuracy. We present a genetic algorithm approach for feature selection and parameters optimization to solve this kind of problem.
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