Performance Evaluation of Activation Functions in Extreme Learning Machine
Karol Struniawski, Aleksandra Konopka, Ryszard Kozera · 2023
This study investigates the performance of 36 different activation functions applied in Extreme Learning Machine on 10 distinct datasets.Results show that Mish and Sexp activation functions exhibit outstanding generalization abilities and consistently perform well across most datasets, while other functions are more dependent on the characteristics of the task at hand.The selection of an activation function is intricately linked to the applied dataset and novel activation functions may possess superior generalization capabilities comparing to commonly employed alternatives.This study provides valuable insight for researchers and practitioners seeking to optimize Extreme Learning Machine performance for solving classification tasks.