Abstract
Extreme learning machine, which has recently lead to gain popularity of single hidden layer feed-forward neural networks, provides a key solution for non-linear problems with least norm and least square solutions at a very low run time. In this work, it is intended to increase the success of hyperspectral image classification with using kernel extreme learning machine. For this purpose, a hybrid kernel is proposed by the convex combination of radial base and polynomial base kernels. In the simulations, Indian Pine hyperspectral image is used and obtained classification results of proposed method are presented with different kernels' results besides results of non-kernel extreme learning machines.
Keywords
Extreme learning machine
Hyperspectral imaging
Kernel (algebra)
Computer science
Artificial intelligence
Polynomial kernel
Pattern recognition (psychology)
Machine learning
Kernel method
Norm (philosophy)
Radial basis function kernel
Artificial neural network
Support vector machine
Mathematics
Subject Areas
Machine Learning and ELM
·Artificial Intelligence ·Physical Sciences
Remote-Sensing Image Classification
·Media Technology ·Physical Sciences
Face and Expression Recognition
·Computer Vision and Pattern Recognition ·Physical Sciences
Citations by Year
2018: 1
2019: 2
2021: 1
2023: 1
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