Functional extreme learning machine for regression and classification

Although Extreme Learning Machine (ELM) can learn thousands of times faster than traditional slow gradient algorithms for training neural networks, ELM fitting accuracy is limited. This paper develops Functional Extreme Learning Machine (FELM), which is a novel regression and classifier. It takes fu...
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Autor*in:	Xianli Liu [verfasserIn] Yongquan Zhou [verfasserIn] Weiping Meng [verfasserIn] Qifang Luo [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	functional neurons functional extreme learning machine parameter learning algorithm extreme learning machine

Übergeordnetes Werk:	In: Mathematical Biosciences and Engineering - AIMS Press, 2020, 20(2023), 2, Seite 3768-3792
Übergeordnetes Werk:	volume:20 ; year:2023 ; number:2 ; pages:3768-3792

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3934/mbe.2023177

Katalog-ID:	DOAJ081298897

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520			\|a Although Extreme Learning Machine (ELM) can learn thousands of times faster than traditional slow gradient algorithms for training neural networks, ELM fitting accuracy is limited. This paper develops Functional Extreme Learning Machine (FELM), which is a novel regression and classifier. It takes functional neurons as the basic computing units and uses functional equation-solving theory to guide the modeling process of functional extreme learning machines. The functional neuron function of FELM is not fixed, and its learning process refers to the process of estimating or adjusting the coefficients. It follows the spirit of extreme learning and solves the generalized inverse of the hidden layer neuron output matrix through the principle of minimum error, without iterating to obtain the optimal hidden layer coefficients. To verify the performance of the proposed FELM, it is compared with ELM, OP-ELM, SVM and LSSVM on several synthetic datasets, XOR problem, benchmark regression and classification datasets. The experimental results show that although the proposed FELM has the same learning speed as ELM, its generalization performance and stability are better than ELM.
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spelling	10.3934/mbe.2023177 doi (DE-627)DOAJ081298897 (DE-599)DOAJ8f365f3bcd784425b080ae26340a261f DE-627 ger DE-627 rakwb eng TP248.13-248.65 QA1-939 Xianli Liu verfasserin aut Functional extreme learning machine for regression and classification 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Although Extreme Learning Machine (ELM) can learn thousands of times faster than traditional slow gradient algorithms for training neural networks, ELM fitting accuracy is limited. This paper develops Functional Extreme Learning Machine (FELM), which is a novel regression and classifier. It takes functional neurons as the basic computing units and uses functional equation-solving theory to guide the modeling process of functional extreme learning machines. The functional neuron function of FELM is not fixed, and its learning process refers to the process of estimating or adjusting the coefficients. It follows the spirit of extreme learning and solves the generalized inverse of the hidden layer neuron output matrix through the principle of minimum error, without iterating to obtain the optimal hidden layer coefficients. To verify the performance of the proposed FELM, it is compared with ELM, OP-ELM, SVM and LSSVM on several synthetic datasets, XOR problem, benchmark regression and classification datasets. The experimental results show that although the proposed FELM has the same learning speed as ELM, its generalization performance and stability are better than ELM. functional neurons functional extreme learning machine parameter learning algorithm extreme learning machine Biotechnology Mathematics Yongquan Zhou verfasserin aut Weiping Meng verfasserin aut Qifang Luo verfasserin aut In Mathematical Biosciences and Engineering AIMS Press, 2020 20(2023), 2, Seite 3768-3792 (DE-627)522894844 (DE-600)2265126-3 15510018 nnns volume:20 year:2023 number:2 pages:3768-3792 https://doi.org/10.3934/mbe.2023177 kostenfrei https://doaj.org/article/8f365f3bcd784425b080ae26340a261f kostenfrei https://www.aimspress.com/article/doi/10.3934/mbe.2023177?viewType=HTML kostenfrei https://doaj.org/toc/1551-0018 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2023 2 3768-3792
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language	English
source	In Mathematical Biosciences and Engineering 20(2023), 2, Seite 3768-3792 volume:20 year:2023 number:2 pages:3768-3792
sourceStr	In Mathematical Biosciences and Engineering 20(2023), 2, Seite 3768-3792 volume:20 year:2023 number:2 pages:3768-3792
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	functional neurons functional extreme learning machine parameter learning algorithm extreme learning machine Biotechnology Mathematics
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authorswithroles_txt_mv	Xianli Liu @@aut@@ Yongquan Zhou @@aut@@ Weiping Meng @@aut@@ Qifang Luo @@aut@@
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callnumber-first	T - Technology
author	Xianli Liu
spellingShingle	Xianli Liu misc TP248.13-248.65 misc QA1-939 misc functional neurons misc functional extreme learning machine misc parameter learning algorithm misc extreme learning machine misc Biotechnology misc Mathematics Functional extreme learning machine for regression and classification
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topic	misc TP248.13-248.65 misc QA1-939 misc functional neurons misc functional extreme learning machine misc parameter learning algorithm misc extreme learning machine misc Biotechnology misc Mathematics
topic_unstemmed	misc TP248.13-248.65 misc QA1-939 misc functional neurons misc functional extreme learning machine misc parameter learning algorithm misc extreme learning machine misc Biotechnology misc Mathematics
topic_browse	misc TP248.13-248.65 misc QA1-939 misc functional neurons misc functional extreme learning machine misc parameter learning algorithm misc extreme learning machine misc Biotechnology misc Mathematics
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title	Functional extreme learning machine for regression and classification
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title_sort	functional extreme learning machine for regression and classification
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title_auth	Functional extreme learning machine for regression and classification
abstract	Although Extreme Learning Machine (ELM) can learn thousands of times faster than traditional slow gradient algorithms for training neural networks, ELM fitting accuracy is limited. This paper develops Functional Extreme Learning Machine (FELM), which is a novel regression and classifier. It takes functional neurons as the basic computing units and uses functional equation-solving theory to guide the modeling process of functional extreme learning machines. The functional neuron function of FELM is not fixed, and its learning process refers to the process of estimating or adjusting the coefficients. It follows the spirit of extreme learning and solves the generalized inverse of the hidden layer neuron output matrix through the principle of minimum error, without iterating to obtain the optimal hidden layer coefficients. To verify the performance of the proposed FELM, it is compared with ELM, OP-ELM, SVM and LSSVM on several synthetic datasets, XOR problem, benchmark regression and classification datasets. The experimental results show that although the proposed FELM has the same learning speed as ELM, its generalization performance and stability are better than ELM.
abstractGer	Although Extreme Learning Machine (ELM) can learn thousands of times faster than traditional slow gradient algorithms for training neural networks, ELM fitting accuracy is limited. This paper develops Functional Extreme Learning Machine (FELM), which is a novel regression and classifier. It takes functional neurons as the basic computing units and uses functional equation-solving theory to guide the modeling process of functional extreme learning machines. The functional neuron function of FELM is not fixed, and its learning process refers to the process of estimating or adjusting the coefficients. It follows the spirit of extreme learning and solves the generalized inverse of the hidden layer neuron output matrix through the principle of minimum error, without iterating to obtain the optimal hidden layer coefficients. To verify the performance of the proposed FELM, it is compared with ELM, OP-ELM, SVM and LSSVM on several synthetic datasets, XOR problem, benchmark regression and classification datasets. The experimental results show that although the proposed FELM has the same learning speed as ELM, its generalization performance and stability are better than ELM.
abstract_unstemmed	Although Extreme Learning Machine (ELM) can learn thousands of times faster than traditional slow gradient algorithms for training neural networks, ELM fitting accuracy is limited. This paper develops Functional Extreme Learning Machine (FELM), which is a novel regression and classifier. It takes functional neurons as the basic computing units and uses functional equation-solving theory to guide the modeling process of functional extreme learning machines. The functional neuron function of FELM is not fixed, and its learning process refers to the process of estimating or adjusting the coefficients. It follows the spirit of extreme learning and solves the generalized inverse of the hidden layer neuron output matrix through the principle of minimum error, without iterating to obtain the optimal hidden layer coefficients. To verify the performance of the proposed FELM, it is compared with ELM, OP-ELM, SVM and LSSVM on several synthetic datasets, XOR problem, benchmark regression and classification datasets. The experimental results show that although the proposed FELM has the same learning speed as ELM, its generalization performance and stability are better than ELM.
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title_short	Functional extreme learning machine for regression and classification
url	https://doi.org/10.3934/mbe.2023177 https://doaj.org/article/8f365f3bcd784425b080ae26340a261f https://www.aimspress.com/article/doi/10.3934/mbe.2023177?viewType=HTML https://doaj.org/toc/1551-0018
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Functional extreme learning machine for regression and classification

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