Advances in extreme learning machines (ELM2010)

摘要

Computational intelligence techniques, especially neural networks, have been attracting a large number of researchers' attentions in the past two decades. It has been well known that conventional learning methods on neural networks have apparent drawbacks and limitations, including: (1) slow learning speed, (2) trivial human tuned parameters, and (3) complicated learning algorithms. Extreme learning machine (ELM) is an emerging learning technique proposed for generalized single-hidden layer feedforward networks (SLFNs). ELM can overcome the above-mentioned drawbacks and limitations of the conventional computational intelligence techniques. Distinguished from the conventional learning theory, the essence of ELM is that the hidden layer of the generalized SLFNs need not be tuned. One of the typical implementations of ELM is that the hidden layer parameters of ELM can be randomly generated.