Abstract
In the classification of hyperspectral images, kernel based approaches have been shown to be successful results. Too much training or testing data in the images increases the computation time and memory requirements in the kernel computations. Extreme learning machines that can be used with the kernel approach also need the same requirements in kernel computations. In this study, improvements were made in terms of computation time and memory using reduced kernel extreme learning machines (RKELM). The obtained results are presented comparatively through the tables of performance and time information with kernel extreme learning machine (KELM).
Keywords
Kernel (algebra)
Computer science
Extreme learning machine
Computation
Artificial intelligence
Tree kernel
Hyperspectral imaging
Pattern recognition (psychology)
Kernel method
Machine learning
Radial basis function kernel
Support vector machine
Algorithm
Mathematics
Artificial neural network
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
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