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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.

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Related Experiment Video

Updated: Jul 2, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Comments on "The extreme learning machine".

Lipo P Wang, Chunru R Wan

IEEE Transactions on Neural Networks

|August 15, 2008

Summary

This summary is machine-generated.

The core concepts of Extreme Learning Machines (ELM) were previously introduced and discussed by researchers like Broomhead, Lowe, and Pao. Therefore, a new designation like "ELM" is not essential for these established machine learning principles.

Related Experiment Videos

Last Updated: Jul 2, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

Machine Learning
Artificial Intelligence
Computational Science

Background:

The recent emergence of Extreme Learning Machines (ELM) has garnered attention in the machine learning community.
This comment addresses the novelty and naming conventions surrounding ELM.

Discussion:

The fundamental principles underlying ELM were previously articulated by Broomhead and Lowe.
Further discussions on these concepts were contributed by Pao and other researchers in the field.
This prior work suggests that the core ideas of ELM are not entirely new.

Key Insights:

The essence of Extreme Learning Machines (ELM) has been previously proposed and discussed in scientific literature.
The terminology 'ELM' may represent a rebranding of existing methodologies rather than a novel advancement.
Prior contributions by Broomhead, Lowe, Pao, and others laid the groundwork for current ELM concepts.

Outlook:

Re-evaluation of the terminology and historical context of machine learning algorithms is crucial.
Promoting clarity and avoiding redundant nomenclature can enhance scientific communication.
Further research should acknowledge and build upon foundational work in the field.