NDLI: A Truly Online Learning Algorithm using Hybrid Fuzzy ARTMAP and Online Extreme Learning Machine for Pattern Classification

Content Provider	Springer Nature Link
Author	Wong, Shen Yuong Yap, Keem Siah Yap, Hwa Jen Tan, Shing Chiang
Copyright Year	2014
Abstract	This paper presents a Hybrid Fuzzy ARTMAP (FAM) and Online Extreme learning machine (OELM), hereafter denoted as FAM-OELM, which enables online learning to start from the first trained data samples without having to set up an initialization phase which requires a chunk of data samples to be ready prior to training. The idea of developing FAM-OELM is motivated by the ELM concept proposed by Huang et al., for being an efficient learning algorithm that provides better generalization performance at a much faster learning speed. However, different from the batch learning ELM and its variant called the online sequential extreme learning machine which still requires an initial offline training phase before it can turn into online training, the proposed FAM-OELM showcases a framework that enable online learning to commence right from the first data sample. Here, classification can be conducted at any time during the training phase. Such appealing feature of the proposed algorithm has strictly fulfilled the criteria of being truly sequential, while many of the existing algorithms are not. In addition, FAM-OELM automatically grows hidden neuron such that the network can accommodate new information without over fitting and compromising on the knowledge learnt earlier. The simulation results reveal the efficacy and validity of FAM-OELM when it is applied to a real world application and various benchmark problems.
Starting Page	585
Ending Page	602
Page Count	18
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	42
Issue Number	3
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2014-08-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Fuzzy ARTMAP (FAM) Online sequential extreme learning machine (OSELM) Online learning Pattern classification Artificial Intelligence (incl. Robotics) Statistical Physics, Dynamical Systems and Complexity Computational Intelligence
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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