Trends in extreme learning machines: a review

Area of Science:

• Machine Learning
• Artificial Intelligence

Background:

• Extreme learning machines (ELM) are gaining significant traction across diverse research domains.
• This review synthesizes recent theoretical and practical advancements in ELM.

Purpose of the Study:

• To provide a comprehensive overview of the current state of Extreme Learning Machines (ELM).
• To explore theoretical foundations, practical improvements, and emerging applications of ELM.

Main Methods:

• Theoretical analysis covering interpolation theory, universal approximation, and generalization capabilities.
• Review of algorithmic enhancements focusing on stability, sparsity, and accuracy.
• Exploration of ELM's extension to new learning tasks like clustering and feature selection.
• Examination of implementation strategies including hardware acceleration and parallel computation.

Main Results:

• ELM demonstrates strong theoretical underpinnings in approximation and generalization.
• Improvements enhance ELM's performance in stability, sparsity, and accuracy.
• ELM has been successfully adapted for a wider range of learning tasks beyond traditional classification and regression.
• Hardware and parallel computing advancements enable ELM for big data and real-time applications.

Conclusions:

• Extreme learning machines offer a powerful and efficient approach to machine learning.
• Ongoing research continues to expand the capabilities and applicability of ELM across various scientific and engineering fields.
• ELM's efficiency, simplicity, and performance make it suitable for domains including biomedical engineering, computer vision, and robotics.