Constrained Extreme Learning Machines: A Study on Classification Cases

Understand

Extreme learning machine (ELM) is an extremely fast learning method and has a powerful performance for pattern recognition tasks proven by enormous researches and engineers.

• However, its good generalization ability is built on large numbers of hidden neurons, which is not beneficial to real time response in the test process.
• In this paper, we proposed new ways, named "constrained extreme learning machines" (CELMs), to randomly select hidden neurons based on sample distribution.
• Compared to completely random selection of hidden nodes in ELM, the CELMs randomly select hidden nodes from the constrained vector space containing some basic combinations of original sample vectors.