A Truly Online Learning Algorithm using Hybrid Fuzzy ARTMAP and Online Extreme Learning Machine for Pattern Classification
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 pr...
| Main Authors: | , , , |
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| Format: | Article |
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Kluwer (now part of Springer)
2015
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| _version_ | 1825721299916292096 |
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| author | Wong, S.Y. Yap, K.S. Yap, H.J. Tan, S.C. |
| author_facet | Wong, S.Y. Yap, K.S. Yap, H.J. Tan, S.C. |
| author_sort | Wong, S.Y. |
| collection | UM |
| description | 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. |
| first_indexed | 2024-03-06T05:48:18Z |
| format | Article |
| id | um.eprints-19440 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:48:18Z |
| publishDate | 2015 |
| publisher | Kluwer (now part of Springer) |
| record_format | dspace |
| spelling | um.eprints-194402018-09-28T01:56:57Z http://eprints.um.edu.my/19440/ A Truly Online Learning Algorithm using Hybrid Fuzzy ARTMAP and Online Extreme Learning Machine for Pattern Classification Wong, S.Y. Yap, K.S. Yap, H.J. Tan, S.C. Q Science (General) T Technology (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering 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. Kluwer (now part of Springer) 2015 Article PeerReviewed Wong, S.Y. and Yap, K.S. and Yap, H.J. and Tan, S.C. (2015) A Truly Online Learning Algorithm using Hybrid Fuzzy ARTMAP and Online Extreme Learning Machine for Pattern Classification. Neural Processing Letters, 42 (3). pp. 585-602. ISSN 1370-4621, DOI https://doi.org/10.1007/s11063-014-9374-5 <https://doi.org/10.1007/s11063-014-9374-5>. http://dx.doi.org/10.1007/s11063-014-9374-5 doi:10.1007/s11063-014-9374-5 |
| spellingShingle | Q Science (General) T Technology (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering Wong, S.Y. Yap, K.S. Yap, H.J. Tan, S.C. A Truly Online Learning Algorithm using Hybrid Fuzzy ARTMAP and Online Extreme Learning Machine for Pattern Classification |
| title | A Truly Online Learning Algorithm using Hybrid Fuzzy ARTMAP and Online Extreme Learning Machine for Pattern Classification |
| title_full | A Truly Online Learning Algorithm using Hybrid Fuzzy ARTMAP and Online Extreme Learning Machine for Pattern Classification |
| title_fullStr | A Truly Online Learning Algorithm using Hybrid Fuzzy ARTMAP and Online Extreme Learning Machine for Pattern Classification |
| title_full_unstemmed | A Truly Online Learning Algorithm using Hybrid Fuzzy ARTMAP and Online Extreme Learning Machine for Pattern Classification |
| title_short | A Truly Online Learning Algorithm using Hybrid Fuzzy ARTMAP and Online Extreme Learning Machine for Pattern Classification |
| title_sort | truly online learning algorithm using hybrid fuzzy artmap and online extreme learning machine for pattern classification |
| topic | Q Science (General) T Technology (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering |
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