CNN classifier using 1D, 2D and 3D feature vectors

Version 1.0.4 (340 ko) par Selva
using CNN network with pre-extracted feature vectors instead of automatically deriving the features by itself from image.
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Mise à jour 16 mai 2019

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CNN deep network consist of inbuilt feature extraction (flattening) layer along with classification layers. By omitting the feature extraction layer (conv layer, Relu layer, pooling layer), we can give features such as GLCM, LBP, MFCC, etc directly to CNN just to classify alone. This can be acheived by building the CNN architecture using fully connected layers alone. This is helpful for classifying audio data.

http://cs231n.github.io/convolutional-networks/ visit this page for doubts regarding the architecture. I have used C->R->F->F->F architecture

Citation pour cette source

Selva (2024). CNN classifier using 1D, 2D and 3D feature vectors (https://www.mathworks.com/matlabcentral/fileexchange/68882-cnn-classifier-using-1d-2d-and-3d-feature-vectors), MATLAB Central File Exchange. Récupéré le .

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Créé avec R2017b
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Version Publié le Notes de version
1.0.4

architecture link added
1.0.3

updated the files
1.0.2

updated files
1.0.1

Added theory
1.0.0