Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/3804
Title: Incremental extreme learning machine
Authors: Chen, Lei
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Issue Date: 2007
Source: Chen, L. (2007). Incremental extreme learning machine. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: This new theory shows that in order to let SLFNs work as universal approximators, one may simply randomly choose input-to-hidden nodes, and then we only need to adjust the output weights linking the hidden layer and the output layer. In such SLFNs implementations, the activation functions for additive nodes can be any bounded nonconstant piecewise continuous functions or the activation functions for RBF nodes can be any integrable piecewise continuous functions.We propose two incremental algorithms:1) Incremental extreme learning machine (I-ELM) 2) Convex I-ELM (CI-ELM).
URI: https://hdl.handle.net/10356/3804
DOI: 10.32657/10356/3804
Rights: Nanyang Technological University
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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