CNNFET: Convolutional neural network feature Extraction Tools
Neither machines nor even human can learn something not represented well enough. Therefore, feature extraction is one of the most important topics in machine learning. Deep convolutional neural networks are able to catch distinguishing features that can represent images or other digital signals. Thi...
Saved in:
| Main Authors: | Huseyin Atasoy, Yakup Kutlu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-05-01
|
| Series: | SoftwareX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S235271102500055X |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Analysis of MSTAR Object Classification Features Extracted by a Deep Convolutional Neural Network
by: I. F. Kupryashkin
Published: (2025-05-01) -
An anti‐jamming method in multistatic radar system based on convolutional neural network
by: Jieyi Liu, et al.
Published: (2022-04-01) -
Generating Human-Interpretable Rules from Convolutional Neural Networks
by: Russel Pears, et al.
Published: (2025-03-01) -
Face recognition method based on convolutional neural network and distributed computing
by: Liu Yanyu, et al.
Published: (2025-04-01) -
An Improved Convolutional Neural Networks: Quantum Pseudo-Transposed Convolutional Neural Networks
by: Li Hai, et al.
Published: (2025-01-01)