This book introduces the newly developed Extreme Learning Machine (ELM) including its theories and learning algorithms. ELM is a unified framework of broad type of generalized single-hidden layer feedforward networks. Unlike traditional popular learning methods, ELM requires less human interventions and can run thousands times faster than those conventional methods. ELM automatically determines all the network parameters analytically, which avoids trivial human intervention and makes it efficient in online and real-time applications. The topics covered in this book are as follow: -Conventional learning theories and learning algorithms; -Learning theory of Extreme learning machine; -Basic extreme learning machine; -Incremental extreme learning machine; -Online sequential extreme learning machine; -Applications of extreme learning machine. Source codes for implementing ELM applications in MATLAB will be included for readers to quickly apply the technique. It is suitable as a project-oriented coursework text for graduate students as well as for researchers or system developers to quickly deploy ELM in actual problem-solving.
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