Enhanced Algorithm for Extreme Learning Machines
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In this paper, we explore enhanced algorithms for Extreme Learning Machines (ELM) and methods to improve their random search capabilities. Extreme Learning Machine is a fast and efficient machine learning algorithm widely applied across various domains. However, it may encounter limitations when processing large-scale datasets. To address this, we propose the EI-ELM main program, which demonstrates superior handling of massive datasets while improving accuracy and generalization performance. The core innovation of EI-ELM lies in its optimized random search algorithm that enhances ELM's performance while maintaining accuracy and stability. This improved algorithm incorporates cutting-edge research in deep learning and neural networks, providing a more robust solution for ELM applications. Key implementation features include adaptive random weight initialization and optimized hidden layer node selection through iterative refinement processes.
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