A Truly Online Learning Algorithm using Hybrid Fuzzy ARTMAP and Online Extreme Learning Machine for Pattern Classification
Document Type
Article
Publication Date
1-1-2015
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.
Keywords
Fuzzy ARTMAP (FAM), Online sequential extreme learning machine (OSELM), Online learning, Pattern classification
Divisions
fac_eng
Funders
University of Malaya Research Collaborative Grant Scheme (PRP-UM-UNITEN), under Grant Number: CG026-2013
Publication Title
Neural Processing Letters
Volume
42
Issue
3
Publisher
Kluwer (now part of Springer)