Radial Basis Function Networks for Convolutional Neural Networks to Learn Similarity Distance Metric and Improve Interpretability

Radial Basis Function Networks for Convolutional Neural Networks to Learn Similarity Distance Metric and Improve Interpretability

Radial basis function neural networks (RBFs) are prime candidates for pattern classification and regression and have been used extensively in classical machine learning applications. However, RBFs have not been integrated into contemporary deep learning research and computer vision using conventiona...

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Bibliographic Details
Main Authors: Mohammadreza Amirian, Friedhelm Schwenker
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Radial basis function neural networks (RBFs)
convolutional neural networks (CNNs)
CNN-RBFs
supervised learning
unsupervised learning
similarity distance metric
Online Access:https://ieeexplore.ieee.org/document/9133368/

Internet

https://ieeexplore.ieee.org/document/9133368/

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