Iterative Least Squares Functional Networks Classifier
This paper proposes unconstrained functional networks as a new classifier to deal with the pattern recognition problems. Both methodology and learning algorithm for this kind of computational intelligence classifier using the iterative least squares optimization criterion are derived. The performanc...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| التنسيق: | article |
| منشور في: |
2007
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://eprints.kfupm.edu.sa/id/eprint/9254/1/ILSF-Abst.pdf https://eprints.kfupm.edu.sa/id/eprint/9254/2/ILSF-Abst.pdf |
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| _version_ | 1864513391197945857 |
|---|---|
| author | Faisal, Kanaan A |
| author2 | unknown |
| author2_role | author |
| author_facet | Faisal, Kanaan A unknown |
| author_role | author |
| dc.creator.none.fl_str_mv | Faisal, Kanaan A unknown |
| dc.date.none.fl_str_mv | 2007-05 2020 |
| dc.format.none.fl_str_mv | text/html application/pdf |
| dc.identifier.none.fl_str_mv | https://eprints.kfupm.edu.sa/id/eprint/9254/1/ILSF-Abst.pdf https://eprints.kfupm.edu.sa/id/eprint/9254/2/ILSF-Abst.pdf (2007) Iterative Least Squares Functional Networks Classifier. IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 18, NO. 3, MAY 2007, 18 (3). pp. 844-850. |
| dc.language.none.fl_str_mv | en en |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.relation.none.fl_str_mv | https://eprints.kfupm.edu.sa/id/eprint/9254/ |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Computer |
| dc.title.none.fl_str_mv | Iterative Least Squares Functional Networks Classifier |
| dc.type.none.fl_str_mv | Article PeerReviewed info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | This paper proposes unconstrained functional networks as a new classifier to deal with the pattern recognition problems. Both methodology and learning algorithm for this kind of computational intelligence classifier using the iterative least squares optimization criterion are derived. The performance of this new intelligent systems scheme is demonstrated and examined using real-world applications. A comparative study with the most common classification algorithms in both machine learning and statistics communities is carried out. The study was achieved with only sets of second-order linearly independent polynomial functions to approximate the neuron functions. The results show that this new framework classifier is reliable, flexible, stable, and achieves a high-quality performance. Index Terms—Functional networks, minimum description length, statistical pattern recognition. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | KFUPM_6c6d3af691cd15b481a7a2a401fe69e1 |
| identifier_str_mv | (2007) Iterative Least Squares Functional Networks Classifier. IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 18, NO. 3, MAY 2007, 18 (3). pp. 844-850. |
| language_invalid_str_mv | en |
| network_acronym_str | KFUPM |
| network_name_str | King Fahd University of Petroleum and Minerals |
| oai_identifier_str | oai::9254 |
| publishDate | 2007 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Iterative Least Squares Functional Networks ClassifierFaisal, Kanaan AunknownComputerThis paper proposes unconstrained functional networks as a new classifier to deal with the pattern recognition problems. Both methodology and learning algorithm for this kind of computational intelligence classifier using the iterative least squares optimization criterion are derived. The performance of this new intelligent systems scheme is demonstrated and examined using real-world applications. A comparative study with the most common classification algorithms in both machine learning and statistics communities is carried out. The study was achieved with only sets of second-order linearly independent polynomial functions to approximate the neuron functions. The results show that this new framework classifier is reliable, flexible, stable, and achieves a high-quality performance. Index Terms—Functional networks, minimum description length, statistical pattern recognition.IEEE2007-052020ArticlePeerReviewedinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articletext/htmlapplication/pdfhttps://eprints.kfupm.edu.sa/id/eprint/9254/1/ILSF-Abst.pdfhttps://eprints.kfupm.edu.sa/id/eprint/9254/2/ILSF-Abst.pdf (2007) Iterative Least Squares Functional Networks Classifier. IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 18, NO. 3, MAY 2007, 18 (3). pp. 844-850. enenhttps://eprints.kfupm.edu.sa/id/eprint/9254/info:eu-repo/semantics/openAccessoai::92542019-11-01T13:46:49Z |
| spellingShingle | Iterative Least Squares Functional Networks Classifier Faisal, Kanaan A Computer |
| status_str | publishedVersion |
| title | Iterative Least Squares Functional Networks Classifier |
| title_full | Iterative Least Squares Functional Networks Classifier |
| title_fullStr | Iterative Least Squares Functional Networks Classifier |
| title_full_unstemmed | Iterative Least Squares Functional Networks Classifier |
| title_short | Iterative Least Squares Functional Networks Classifier |
| title_sort | Iterative Least Squares Functional Networks Classifier |
| topic | Computer |
| url | https://eprints.kfupm.edu.sa/id/eprint/9254/1/ILSF-Abst.pdf https://eprints.kfupm.edu.sa/id/eprint/9254/2/ILSF-Abst.pdf |