Compact extreme learning machines for biological systems
In biological system modelling using data-driven black-box methods, it is essential to effectively and efficiently produce a parsimonious model to represent the system behaviour. The Extreme Learning Machine (ELM) is a recent development in fast learning paradigms. However, the derived model is not...
Uloženo v:
| Vydáno v: | International journal of computational biology and drug design Ročník 3; číslo 2; s. 112 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
England
2010
|
| Témata: | |
| ISSN: | 1756-0756 |
| On-line přístup: | Zjistit podrobnosti o přístupu |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | In biological system modelling using data-driven black-box methods, it is essential to effectively and efficiently produce a parsimonious model to represent the system behaviour. The Extreme Learning Machine (ELM) is a recent development in fast learning paradigms. However, the derived model is not necessarily sparse. In this paper, an improved ELM is investigated, aiming to obtain a more compact model without significantly increasing the overall computational complexity. This is achieved by associating each model term to a regularized parameter, thus insignificant ones are automatically unselected, leading to improved model sparsity. Experimental results on biochemical data confirm its effectiveness. |
|---|---|
| AbstractList | In biological system modelling using data-driven black-box methods, it is essential to effectively and efficiently produce a parsimonious model to represent the system behaviour. The Extreme Learning Machine (ELM) is a recent development in fast learning paradigms. However, the derived model is not necessarily sparse. In this paper, an improved ELM is investigated, aiming to obtain a more compact model without significantly increasing the overall computational complexity. This is achieved by associating each model term to a regularized parameter, thus insignificant ones are automatically unselected, leading to improved model sparsity. Experimental results on biochemical data confirm its effectiveness.In biological system modelling using data-driven black-box methods, it is essential to effectively and efficiently produce a parsimonious model to represent the system behaviour. The Extreme Learning Machine (ELM) is a recent development in fast learning paradigms. However, the derived model is not necessarily sparse. In this paper, an improved ELM is investigated, aiming to obtain a more compact model without significantly increasing the overall computational complexity. This is achieved by associating each model term to a regularized parameter, thus insignificant ones are automatically unselected, leading to improved model sparsity. Experimental results on biochemical data confirm its effectiveness. In biological system modelling using data-driven black-box methods, it is essential to effectively and efficiently produce a parsimonious model to represent the system behaviour. The Extreme Learning Machine (ELM) is a recent development in fast learning paradigms. However, the derived model is not necessarily sparse. In this paper, an improved ELM is investigated, aiming to obtain a more compact model without significantly increasing the overall computational complexity. This is achieved by associating each model term to a regularized parameter, thus insignificant ones are automatically unselected, leading to improved model sparsity. Experimental results on biochemical data confirm its effectiveness. |
| Author | Du, Da-Jun Li, Kang Li, Yurong He, Hai-Bo Deng, Jing |
| Author_xml | – sequence: 1 givenname: Kang surname: Li fullname: Li, Kang email: k.li@qub.ac.uk organization: School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UK. k.li@qub.ac.uk – sequence: 2 givenname: Jing surname: Deng fullname: Deng, Jing – sequence: 3 givenname: Hai-Bo surname: He fullname: He, Hai-Bo – sequence: 4 givenname: Yurong surname: Li fullname: Li, Yurong – sequence: 5 givenname: Da-Jun surname: Du fullname: Du, Da-Jun |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/20852336$$D View this record in MEDLINE/PubMed |
| BookMark | eNo1jz1PwzAYhD0U0Q_4AwwoG1OKXztOnBHSAkWVWGCObOdNCYrjYicS_fdYoix30unR6W5JZoMbkJAboGsQNLvfvVaPm82a0RhQLhiXM7KAQuQpjTInyxC-KM2ZhPySzBmVEeH5gsjK2aMyY4I_o0eLSY_KD91wSKwyn92AIWmdT3TnenfojOqTcAoj2nBFLlrVB7w--4p8PG3fq5d0__a8qx72qclAytS0gJwVBctoBiYvWh1zA8CwjBMw1xxLikxIjrrRKDQwzaGFOB0bTQu2Ind_vUfvvicMY227YLDv1YBuCnUhBJScl2Ukb8_kpC029dF3VvlT_X-W_QJkzlY1 |
| CitedBy_id | crossref_primary_10_1016_j_neucom_2012_11_030 crossref_primary_10_1016_j_foodcont_2017_02_045 crossref_primary_10_1002_minf_201501008 crossref_primary_10_1155_2016_5197932 |
| ContentType | Journal Article |
| DBID | CGR CUY CVF ECM EIF NPM 7X8 |
| DOI | 10.1504/IJCBDD.2010.035238 |
| DatabaseName | Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
| DatabaseTitle | MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | no_fulltext_linktorsrc |
| ExternalDocumentID | 20852336 |
| Genre | Journal Article |
| GroupedDBID | --- 0R~ 29J 4.4 5GY ABJNI ACGFS ACIWK ACPRK AFRAH ALMA_UNASSIGNED_HOLDINGS ALSBL CGR CS3 CUY CVF DU5 EBS ECM EIF EJD F5P H13 HZ~ MET MIE NPM O9- P2P RTD 7X8 |
| ID | FETCH-LOGICAL-c4188-cf1e327724041c67fbc41c112e9852e6b3e90e2583ebdbe5b12b31f1175edb072 |
| IEDL.DBID | 7X8 |
| ISSN | 1756-0756 |
| IngestDate | Fri Jul 11 11:20:41 EDT 2025 Thu Jan 02 22:06:16 EST 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c4188-cf1e327724041c67fbc41c112e9852e6b3e90e2583ebdbe5b12b31f1175edb072 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| OpenAccessLink | https://digitalcommons.uri.edu/ele_facpubs/618 |
| PMID | 20852336 |
| PQID | 755193399 |
| PQPubID | 23479 |
| ParticipantIDs | proquest_miscellaneous_755193399 pubmed_primary_20852336 |
| PublicationCentury | 2000 |
| PublicationDate | 2010-00-00 20100101 |
| PublicationDateYYYYMMDD | 2010-01-01 |
| PublicationDate_xml | – year: 2010 text: 2010-00-00 |
| PublicationDecade | 2010 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England |
| PublicationTitle | International journal of computational biology and drug design |
| PublicationTitleAlternate | Int J Comput Biol Drug Des |
| PublicationYear | 2010 |
| SSID | ssj0062816 |
| Score | 1.7773858 |
| Snippet | In biological system modelling using data-driven black-box methods, it is essential to effectively and efficiently produce a parsimonious model to represent... |
| SourceID | proquest pubmed |
| SourceType | Aggregation Database Index Database |
| StartPage | 112 |
| SubjectTerms | Algorithms Artificial Intelligence Computer Simulation Humans Models, Biological Neural Networks (Computer) Systems Biology - methods |
| Title | Compact extreme learning machines for biological systems |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/20852336 https://www.proquest.com/docview/755193399 |
| Volume | 3 |
| hasFullText | |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV07T8MwELaAMrDwEK_ykgfWqI7fmRC0VICg6gCoWxQ_ghiaFlL4_ZwTtxtiYIgHS46iu_PdF9_5PoQuibCsAMefwFMmnHKdGOspKMTBNHfENPwpr49qNNKTSTaOtTl1LKtc-sTGUbuZDWfkPSUC1oBwejX_SAJpVEiuRgaNddRhgGRCRZearJIIkuqG-RQCJPw0wxDvzAjCe_cP_ZvBIFZ2AQZh-neE2USa4c4_v3EXbUeIia9bm9hDa77aR7rZ-HaBwRmHI0Ec6SLe8LQpp_Q1BviK255MQXG47fFcH6CX4e1z_y6JrAmJ5SmYvS1TzyiAZk54aqUqDcxbgFU-04J6aZjPiKdCM2-c8cKk1LC0DC07vTNE0UO0Uc0qf4wwz5zkBWAwoyyXVmQpvEuSzLECFhSqi_BSDjlYZUg1FJWffdX5ShJddNTKMp-33TPyQApKGZMnfy8-RVttsj6ceJyhTgk70p-jTfu9eK8_LxptwzgaP_0A3Tqy0Q |
| linkProvider | ProQuest |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Compact+extreme+learning+machines+for+biological+systems&rft.jtitle=International+journal+of+computational+biology+and+drug+design&rft.au=Li%2C+Kang&rft.au=Deng%2C+Jing&rft.au=He%2C+Hai-Bo&rft.au=Li%2C+Yurong&rft.date=2010-01-01&rft.issn=1756-0756&rft.volume=3&rft.issue=2&rft.spage=112&rft_id=info:doi/10.1504%2FIJCBDD.2010.035238&rft_id=info%3Apmid%2F20852336&rft_id=info%3Apmid%2F20852336&rft.externalDocID=20852336 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1756-0756&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1756-0756&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1756-0756&client=summon |