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Dr. Kawecki says it is worth investigating whether humans also pay hidden costs for extreme learning.
Traveling wave frequencies and an extreme learning machine (ELM) were used to determine fault location.
Therefore, we suggest a new learning algorithm for CNNs using an extreme learning machine (ELM).
After obtaining the represented of time sequence, an extreme learning machine is utilized for classification.
Here a novel learning algorithm called extreme learning machines (ELM) is used.
The hidden node parameters are modified using extreme learning machine (ELM) by assigning random values.
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This structure extends simplified learning introduced by Extreme Learning Machine and Reservoir Computing techniques to the field of images.
In this study, Extreme Learning Machine (ELM) methods were used for the classification of Internet traffic.
In this paper, we propose an Extreme Learning Machine (ELM) approach for large-scale data analytic.
Instead of defining transition rules, the Extreme Learning Machine (ELM) was applied in this study.
Three cascade combinations are tested with the Extreme Learning Machine as the underlying classification architecture.
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