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

버전 1.0.4 (340 KB) 작성자: Selva
using CNN network with pre-extracted feature vectors instead of automatically deriving the features by itself from image.

다운로드 수: 2K

업데이트 날짜: 2019/5/16

라이선스 보기

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

인용 양식

Selva (2022). 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. 검색됨 .

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