Online learning using deep random vector functional link network
<p>Deep neural networks have shown their promise in recent years with their state-of-the-art results. Yet, backpropagation-based methods may suffer from time-consuming training process and catastrophic forgetting when performing online learning. In this work we attempt to curtail them by emplo...
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| مؤلفون آخرون: | , |
| منشور في: |
2023
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| _version_ | 1864513529420185600 |
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| author | Sreenivasan Shiva (17823755) |
| author2 | Minghui Hu (2457952) Ponnuthurai Nagaratnam Suganthan (11274636) |
| author2_role | author author |
| author_facet | Sreenivasan Shiva (17823755) Minghui Hu (2457952) Ponnuthurai Nagaratnam Suganthan (11274636) |
| author_role | author |
| dc.creator.none.fl_str_mv | Sreenivasan Shiva (17823755) Minghui Hu (2457952) Ponnuthurai Nagaratnam Suganthan (11274636) |
| dc.date.none.fl_str_mv | 2023-10-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.engappai.2023.106676 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Online_learning_using_deep_random_vector_functional_link_network/25038524 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Data management and data science Machine learning Ensemble deep random vector functional link Extreme learning machine Online learning |
| dc.title.none.fl_str_mv | Online learning using deep random vector functional link network |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Deep neural networks have shown their promise in recent years with their state-of-the-art results. Yet, backpropagation-based methods may suffer from time-consuming training process and catastrophic forgetting when performing online learning. In this work we attempt to curtail them by employing the ensemble deep Random Vector Functional Link (edRVFL). As opposed to backpropagation-based neural networks that adjust weights iteratively, RVFL uses a closed-form solution method without iterative parameter learning. In addition, our approach allows the model to grow incrementally as new data is made available so that it can more resemble real-life learning scenarios. Our proposed online learning models were able to perform better on 72% of the datasets in the classification scenario and 80% of the datasets in the regression scenario, when compared to other available randomization-based online learning models in the literature. This is further supported by statistical comparisons which also show the stability of our network.</p><h2>Other Information</h2> <p> Published in: Engineering Applications of Artificial Intelligence<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.engappai.2023.106676" target="_blank">https://dx.doi.org/10.1016/j.engappai.2023.106676</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_5638ae2047ec6395b6a3df85ee4e03fd |
| identifier_str_mv | 10.1016/j.engappai.2023.106676 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25038524 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Online learning using deep random vector functional link networkSreenivasan Shiva (17823755)Minghui Hu (2457952)Ponnuthurai Nagaratnam Suganthan (11274636)Information and computing sciencesArtificial intelligenceData management and data scienceMachine learningEnsemble deep random vector functional linkExtreme learning machineOnline learning<p>Deep neural networks have shown their promise in recent years with their state-of-the-art results. Yet, backpropagation-based methods may suffer from time-consuming training process and catastrophic forgetting when performing online learning. In this work we attempt to curtail them by employing the ensemble deep Random Vector Functional Link (edRVFL). As opposed to backpropagation-based neural networks that adjust weights iteratively, RVFL uses a closed-form solution method without iterative parameter learning. In addition, our approach allows the model to grow incrementally as new data is made available so that it can more resemble real-life learning scenarios. Our proposed online learning models were able to perform better on 72% of the datasets in the classification scenario and 80% of the datasets in the regression scenario, when compared to other available randomization-based online learning models in the literature. This is further supported by statistical comparisons which also show the stability of our network.</p><h2>Other Information</h2> <p> Published in: Engineering Applications of Artificial Intelligence<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.engappai.2023.106676" target="_blank">https://dx.doi.org/10.1016/j.engappai.2023.106676</a></p>2023-10-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.engappai.2023.106676https://figshare.com/articles/journal_contribution/Online_learning_using_deep_random_vector_functional_link_network/25038524CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/250385242023-10-01T00:00:00Z |
| spellingShingle | Online learning using deep random vector functional link network Sreenivasan Shiva (17823755) Information and computing sciences Artificial intelligence Data management and data science Machine learning Ensemble deep random vector functional link Extreme learning machine Online learning |
| status_str | publishedVersion |
| title | Online learning using deep random vector functional link network |
| title_full | Online learning using deep random vector functional link network |
| title_fullStr | Online learning using deep random vector functional link network |
| title_full_unstemmed | Online learning using deep random vector functional link network |
| title_short | Online learning using deep random vector functional link network |
| title_sort | Online learning using deep random vector functional link network |
| topic | Information and computing sciences Artificial intelligence Data management and data science Machine learning Ensemble deep random vector functional link Extreme learning machine Online learning |