Flexible Clustered Multi-Task Learning by Learning Representative Tasks
Multi-task learning (MTL) methods have shown promising performance by learning multiple relevant tasks simultaneously, which exploits to share useful information across relevant tasks. Among various MTL methods, clustered multi-task learning (CMTL) assumes that all tasks can be clustered into groups...
Uložené v:
| Vydané v: | IEEE transactions on pattern analysis and machine intelligence Ročník 38; číslo 2; s. 266 - 278 |
|---|---|
| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
United States
IEEE
01.02.2016
|
| Predmet: | |
| ISSN: | 0162-8828, 1939-3539, 2160-9292, 1939-3539 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Multi-task learning (MTL) methods have shown promising performance by learning multiple relevant tasks simultaneously, which exploits to share useful information across relevant tasks. Among various MTL methods, clustered multi-task learning (CMTL) assumes that all tasks can be clustered into groups and attempts to learn the underlying cluster structure from the training data. In this paper, we present a new approach for CMTL, called flexible clustered multi-task (FCMTL), in which the cluster structure is learned by identifying representative tasks. The new approach allows an arbitrary task to be described by multiple representative tasks, effectively soft-assigning a task to multiple clusters with different weights. Unlike existing counterpart, the proposed approach is more flexible in that (a) it does not require clusters to be disjoint, (b) tasks within one particular cluster do not have to share information to the same extent, and (c) the number of clusters is automatically inferred from data. Computationally, the proposed approach is formulated as a row-sparsity pursuit problem. We validate the proposed FCMTL on both synthetic and real-world data sets, and empirical results demonstrate that it outperforms many existing MTL methods. |
|---|---|
| AbstractList | Multi-task learning (MTL) methods have shown promising performance by learning multiple relevant tasks simultaneously, which exploits to share useful information across relevant tasks. Among various MTL methods, clustered multi-task learning (CMTL) assumes that all tasks can be clustered into groups and attempts to learn the underlying cluster structure from the training data. In this paper, we present a new approach for CMTL, called flexible clustered multi-task (FCMTL), in which the cluster structure is learned by identifying representative tasks. The new approach allows an arbitrary task to be described by multiple representative tasks, effectively soft-assigning a task to multiple clusters with different weights. Unlike existing counterpart, the proposed approach is more flexible in that (a) it does not require clusters to be disjoint, (b) tasks within one particular cluster do not have to share information to the same extent, and (c) the number of clusters is automatically inferred from data. Computationally, the proposed approach is formulated as a row-sparsity pursuit problem. We validate the proposed FCMTL on both synthetic and real-world data sets, and empirical results demonstrate that it outperforms many existing MTL methods.Multi-task learning (MTL) methods have shown promising performance by learning multiple relevant tasks simultaneously, which exploits to share useful information across relevant tasks. Among various MTL methods, clustered multi-task learning (CMTL) assumes that all tasks can be clustered into groups and attempts to learn the underlying cluster structure from the training data. In this paper, we present a new approach for CMTL, called flexible clustered multi-task (FCMTL), in which the cluster structure is learned by identifying representative tasks. The new approach allows an arbitrary task to be described by multiple representative tasks, effectively soft-assigning a task to multiple clusters with different weights. Unlike existing counterpart, the proposed approach is more flexible in that (a) it does not require clusters to be disjoint, (b) tasks within one particular cluster do not have to share information to the same extent, and (c) the number of clusters is automatically inferred from data. Computationally, the proposed approach is formulated as a row-sparsity pursuit problem. We validate the proposed FCMTL on both synthetic and real-world data sets, and empirical results demonstrate that it outperforms many existing MTL methods. Multi-task learning (MTL) methods have shown promising performance by learning multiple relevant tasks simultaneously, which exploits to share useful information across relevant tasks. Among various MTL methods, clustered multi-task learning (CMTL) assumes that all tasks can be clustered into groups and attempts to learn the underlying cluster structure from the training data. In this paper, we present a new approach for CMTL, called flexible clustered multi-task (FCMTL), in which the cluster structure is learned by identifying representative tasks. The new approach allows an arbitrary task to be described by multiple representative tasks, effectively soft-assigning a task to multiple clusters with different weights. Unlike existing counterpart, the proposed approach is more flexible in that (a) it does not require clusters to be disjoint, (b) tasks within one particular cluster do not have to share information to the same extent, and (c) the number of clusters is automatically inferred from data. Computationally, the proposed approach is formulated as a row-sparsity pursuit problem. We validate the proposed FCMTL on both synthetic and real-world data sets, and empirical results demonstrate that it outperforms many existing MTL methods. |
| Author | Qiang Zhou Qi Zhao |
| Author_xml | – sequence: 1 givenname: Qiang surname: Zhou fullname: Zhou, Qiang – sequence: 2 givenname: Qi surname: Zhao fullname: Zhao, Qi |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26761733$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kE1Lw0AURQep2A_9AwqSpZvUeTNJZrIsxdZCiyJ1PUySFxlNkzqTiP33JrZVcOHqvcU5F-4dkl5ZlUjIJdAxAI1v14-T1WLMKIRjFoQsBjghAwYR9WMWsx4ZUIiYLyWTfTJ07pVSCELKz0ifRSICwfmAzGcFfpqkQG9aNK5Gi5m3aora-Gvt3rwlalua8sVLdr__E24tOixrXZsP9DrQnZPTXBcOLw53RJ5nd-vpvb98mC-mk6WfcsZrP5OQ0iTXDGLIAikgzzTQiAcaEsHTUKNIYip5AoGMUxohS7IUNWYsDJiOOB-Rm33u1lbvDbpabYxLsSh0iVXjFIiItrGCiRa9PqBNssFMba3ZaLtTx_ItIPdAaivnLOYqNV2nqqytNoUCqrqd1ffOqttZHXZuVfZHPab_K13tJYOIP4KAkAYQ8i-Cbogk |
| CODEN | ITPIDJ |
| CitedBy_id | crossref_primary_10_1109_TNNLS_2017_2651068 crossref_primary_10_1007_s12021_018_9381_1 crossref_primary_10_1016_j_compag_2018_10_019 crossref_primary_10_1016_j_patcog_2018_10_023 crossref_primary_10_1016_j_neunet_2019_02_007 crossref_primary_10_1016_j_compchemeng_2021_107320 crossref_primary_10_1109_TMM_2021_3055959 crossref_primary_10_1016_j_ifacol_2020_12_020 crossref_primary_10_1109_TPAMI_2017_2688363 crossref_primary_10_1016_j_neucom_2021_12_048 crossref_primary_10_3390_photonics12040324 crossref_primary_10_1016_j_neucom_2024_127259 crossref_primary_10_3390_s17102218 crossref_primary_10_1109_TMM_2022_3147664 crossref_primary_10_1016_j_csda_2024_107956 crossref_primary_10_1109_TKDE_2024_3372462 crossref_primary_10_1007_s00530_017_0534_0 crossref_primary_10_1016_j_neucom_2024_129136 crossref_primary_10_1016_j_neunet_2024_106619 crossref_primary_10_1016_j_neucom_2023_02_023 crossref_primary_10_1109_TKDE_2021_3070203 crossref_primary_10_1109_TCYB_2018_2864107 crossref_primary_10_1109_TPAMI_2021_3058852 crossref_primary_10_1109_TNNLS_2020_3028453 crossref_primary_10_1109_TFUZZ_2021_3062691 crossref_primary_10_1109_TKDE_2019_2937026 crossref_primary_10_1016_j_patcog_2018_12_018 crossref_primary_10_1016_j_neucom_2023_126237 crossref_primary_10_1109_TNNLS_2020_3026532 crossref_primary_10_1007_s00521_022_07126_3 crossref_primary_10_1109_JLT_2022_3224797 crossref_primary_10_1109_TPAMI_2020_2991344 crossref_primary_10_1007_s10489_022_04020_2 crossref_primary_10_1109_ACCESS_2024_3376441 crossref_primary_10_1109_TNNLS_2020_3042500 crossref_primary_10_1109_TSP_2021_3078625 crossref_primary_10_1007_s11222_024_10550_1 crossref_primary_10_1109_JPHOT_2021_3056471 |
| Cites_doi | 10.1109/TPAMI.2008.297 10.1145/1553374.1553431 10.1145/2339530.2339672 10.1023/A:1007379606734 10.1145/1014052.1014067 10.1007/s10994-007-5040-8 10.1109/TNN.2010.2095882 10.1109/TKDE.2009.142 10.1109/TPAMI.2012.189 10.1109/TNNLS.2012.2200262 10.1109/CVPR.2010.5540018 10.1016/j.neucom.2013.02.024 10.1109/CVPR.2012.6247852 10.1007/s10107-012-0530-2 10.1093/bioinformatics/btm611 10.1137/080716542 10.1145/2020408.2020423 10.1109/TNN.2011.2157521 10.1111/j.1467-9868.2005.00532.x 10.1007/s10107-012-0629-5 10.1145/2020408.2020549 10.1561/2200000016 10.1109/CVPR.2010.5539975 10.1162/089976603762553013 10.1017/CBO9780511804441 10.1109/TPAMI.2007.1055 10.1145/1835804.1835952 10.1145/1102351.1102479 10.1109/CVPR.2005.177 |
| ContentType | Journal Article |
| DBID | 97E RIA RIE AAYXX CITATION NPM 7X8 |
| DOI | 10.1109/TPAMI.2015.2452911 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef PubMed MEDLINE - Academic |
| DatabaseTitle | CrossRef PubMed MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic PubMed |
| 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: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science |
| EISSN | 2160-9292 1939-3539 |
| EndPage | 278 |
| ExternalDocumentID | 26761733 10_1109_TPAMI_2015_2452911 7150415 |
| Genre | orig-research Research Support, Non-U.S. Gov't Journal Article |
| GrantInformation_xml | – fundername: Defense Innovative Research Programme grantid: 9014100596 |
| GroupedDBID | --- -DZ -~X .DC 0R~ 29I 4.4 53G 5GY 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK ACNCT AENEX AGQYO AGSQL AHBIQ AKQYR ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 E.L EBS EJD F5P HZ~ IEDLZ IFIPE IPLJI JAVBF LAI M43 MS~ O9- OCL P2P PQQKQ RIA RIE RNS RXW TAE TN5 UHB ~02 AAYXX CITATION 5VS 9M8 ABFSI ADRHT AETEA AETIX AI. AIBXA AKJIK ALLEH FA8 H~9 IBMZZ ICLAB IFJZH NPM RIG RNI RZB VH1 XJT 7X8 |
| ID | FETCH-LOGICAL-c323t-d81c0bfa2191d4871fda10634a1b73c5ae7b9083b1489c06e2bdceaed2542a633 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 61 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000369989600006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0162-8828 1939-3539 |
| IngestDate | Thu Oct 02 12:17:12 EDT 2025 Mon Jul 21 06:07:33 EDT 2025 Sat Nov 29 05:15:56 EST 2025 Tue Nov 18 22:07:02 EST 2025 Wed Aug 27 02:47:51 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Keywords | representative task group sparsity Clustered multi-task learning |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c323t-d81c0bfa2191d4871fda10634a1b73c5ae7b9083b1489c06e2bdceaed2542a633 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| PMID | 26761733 |
| PQID | 1760871727 |
| PQPubID | 23479 |
| PageCount | 13 |
| ParticipantIDs | pubmed_primary_26761733 proquest_miscellaneous_1760871727 ieee_primary_7150415 crossref_citationtrail_10_1109_TPAMI_2015_2452911 crossref_primary_10_1109_TPAMI_2015_2452911 |
| PublicationCentury | 2000 |
| PublicationDate | 2016-Feb.-1 2016-2-1 2016-Feb 20160201 |
| PublicationDateYYYYMMDD | 2016-02-01 |
| PublicationDate_xml | – month: 02 year: 2016 text: 2016-Feb.-1 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | IEEE transactions on pattern analysis and machine intelligence |
| PublicationTitleAbbrev | TPAMI |
| PublicationTitleAlternate | IEEE Trans Pattern Anal Mach Intell |
| PublicationYear | 2016 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| References | zhang (ref58) 0 ref13 ref56 ref12 ref59 ref15 elhamifar (ref18) 0 ref53 ref11 ref54 ref10 gong (ref23) 0 ref17 kim (ref33) 0 ref16 bonilla (ref6) 0 ref19 passos (ref38) 0 romera-paredes (ref43) 0 ref47 zhong (ref60) 0 ref41 welinder (ref50) 2010 quadrianto (ref40) 0 jacob (ref26) 0 ref49 zhang (ref55) 0 lee (ref35) 0 ref8 jalali (ref29) 0 ref9 gong (ref25) 2013; 14 ref3 chen (ref14) 0 ref5 liu (ref36) 0 romera-paredes (ref42) 0 kang (ref30) 0 ref37 ref31 ref32 wah (ref48) 2011 xue (ref51) 2007; 8 ref39 yang (ref52) 0 schwaighofer (ref44) 2004 bakker (ref4) 2003; 4 zhou (ref61) 0 zhang (ref57) 2013 ref24 evgeniou (ref20) 2005; 6 ref22 ref21 ref28 ref27 bonilla (ref7) 0 kumar (ref34) 0 solnon (ref45) 2012; 13 ref62 ando (ref1) 2005; 6 argyriou (ref2) 0 sra (ref46) 2012 |
| References_xml | – ident: ref39 doi: 10.1109/TPAMI.2008.297 – ident: ref27 doi: 10.1145/1553374.1553431 – start-page: 733 year: 0 ident: ref55 article-title: A convex formulation for learning task relationships in multi-task learning publication-title: Proc 26th Conf Uncertainty Artif Intell – ident: ref24 doi: 10.1145/2339530.2339672 – year: 2010 ident: ref50 article-title: Caltech-UCSD Birds 200 – ident: ref10 doi: 10.1023/A:1007379606734 – ident: ref21 doi: 10.1145/1014052.1014067 – start-page: 521 year: 0 ident: ref30 article-title: Learning with whom to share in multi-task feature learning publication-title: Proc 28th Int Conf Mach Learn – ident: ref3 doi: 10.1007/s10994-007-5040-8 – start-page: 702 year: 0 ident: ref61 article-title: Clustered multi-task learning via alternating structure optimization publication-title: Proc Adv Neural Inf Process Syst – year: 0 ident: ref7 article-title: Multi-task Gaussian process prediction publication-title: Proc Adv Neural Inf Process Syst – ident: ref17 doi: 10.1109/TNN.2010.2095882 – ident: ref31 doi: 10.1109/TKDE.2009.142 – ident: ref12 doi: 10.1109/TPAMI.2012.189 – ident: ref59 doi: 10.1109/TNNLS.2012.2200262 – ident: ref49 doi: 10.1109/CVPR.2010.5540018 – start-page: 1383 year: 0 ident: ref34 article-title: Learning task grouping and overlap in multi-task learning publication-title: Proc 29th Int Conf Mach Learn – volume: 8 start-page: 35 year: 2007 ident: ref51 article-title: Multi-task learning for classification with Dirichlet process priors publication-title: J Mach Learn Res – ident: ref16 doi: 10.1016/j.neucom.2013.02.024 – start-page: 105 year: 0 ident: ref14 article-title: Smoothing proximal gradient method for general structured sparse learning publication-title: Proc Conf Uncertainty Artif Intell – year: 0 ident: ref2 article-title: Multi-task feature learning publication-title: Proc Adv Neural Inf Process Syst – ident: ref19 doi: 10.1109/CVPR.2012.6247852 – ident: ref22 doi: 10.1007/s10107-012-0530-2 – ident: ref28 doi: 10.1093/bioinformatics/btm611 – start-page: 1103 year: 0 ident: ref38 article-title: Flexible modeling of latent task structures in multitask learning publication-title: Proc Int Conf Mach Learn – year: 2004 ident: ref44 article-title: Learning Gaussian process kernels via hierarchical Bayes publication-title: Proc Adv Neural Inf Process Syst – start-page: 339 year: 0 ident: ref36 article-title: Multi-task feature learning via efficient l2, 1-norm minimization publication-title: Proc Conf Uncertainty Artif Intell – start-page: 19 year: 0 ident: ref18 article-title: Finding exemplars from pairwise dissimilarities via simultaneous sparse recovery publication-title: Proc Adv Neural Inf Process Syst – volume: 14 start-page: 2979 year: 2013 ident: ref25 article-title: Multi-stage multi-task feature learning publication-title: J Mach Learn Res – volume: 6 start-page: 615 year: 2005 ident: ref20 article-title: Learning multiple tasks with kernel methods publication-title: J Mach Learn Res – year: 0 ident: ref35 article-title: Adaptive multi-task lasso: With application to eqtl detection publication-title: Proc Adv Neural Inf Process Syst – ident: ref5 doi: 10.1137/080716542 – ident: ref13 doi: 10.1145/2020408.2020423 – ident: ref41 doi: 10.1109/TNN.2011.2157521 – volume: 6 start-page: 1817 year: 2005 ident: ref1 article-title: A framework for learning predictive structures from multiple tasks and unlabeled data publication-title: J Mach Learn Res – year: 0 ident: ref23 article-title: Multi-stage multi-task feature learning publication-title: Proc Adv Neural Inf Process Syst – year: 2012 ident: ref46 publication-title: Optimization for Machine Learning – start-page: 1444 year: 0 ident: ref43 article-title: Multilinear multitask learning publication-title: Proc 30th Int Conf Mach Learn – ident: ref54 doi: 10.1111/j.1467-9868.2005.00532.x – start-page: 543 year: 0 ident: ref33 article-title: Tree-guided group lasso for multi-task regression with structured sparsity publication-title: Proc 27th Int Conf Mach Learn – ident: ref37 doi: 10.1007/s10107-012-0629-5 – ident: ref62 doi: 10.1145/2020408.2020549 – ident: ref8 doi: 10.1561/2200000016 – year: 0 ident: ref42 article-title: Exploiting unrelated tasks in multi-task learning publication-title: Proc Int Conf Artif Intell Statist – year: 0 ident: ref58 article-title: Probabilistic multi-task feature selection publication-title: Proc Adv Neural Inf Process Syst – ident: ref56 doi: 10.1109/CVPR.2010.5539975 – ident: ref32 doi: 10.1162/089976603762553013 – start-page: 2151 year: 0 ident: ref52 article-title: Heterogeneous multitask learning with joint sparsity constraints publication-title: Proc Adv Neural Inf Process Syst – start-page: 43 year: 0 ident: ref6 article-title: Kernel multi-task learning using task-specific features publication-title: Proc 11th Int Conf Artif Intell Statist – ident: ref9 doi: 10.1017/CBO9780511804441 – start-page: 49 year: 0 ident: ref60 article-title: Convex multitask learning with flexible task clusters publication-title: Proc 29th Int Conf Mach Learn – year: 0 ident: ref29 article-title: A dirty model for multi-task learning publication-title: Proc Adv Neural Inf Process Syst – ident: ref47 doi: 10.1109/TPAMI.2007.1055 – start-page: 1917 year: 2013 ident: ref57 article-title: Learning high-order task relationships in multi-task learning publication-title: Proc 23rd Int Joint Conf Artif Intell – ident: ref11 doi: 10.1145/1835804.1835952 – volume: 4 start-page: 83 year: 2003 ident: ref4 article-title: Task clustering and gating for Bayesian multitask learning publication-title: J Mach Learn Res – ident: ref53 doi: 10.1145/1102351.1102479 – volume: 13 start-page: 2773 year: 2012 ident: ref45 article-title: Multi-task regression using minimal penalties publication-title: J Mach Learn Res – year: 0 ident: ref26 article-title: Clustered multi-task learning: A convex formulation publication-title: Proc Adv Neural Inf Process Syst – ident: ref15 doi: 10.1109/CVPR.2005.177 – year: 2011 ident: ref48 article-title: The Caltech-UCSD Birds-200-2011 Dataset – year: 0 ident: ref40 article-title: Multitask learning without label correspondences publication-title: Proc Adv Neural Inf Process Syst |
| SSID | ssj0014503 |
| Score | 2.457809 |
| Snippet | Multi-task learning (MTL) methods have shown promising performance by learning multiple relevant tasks simultaneously, which exploits to share useful... |
| SourceID | proquest pubmed crossref ieee |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 266 |
| SubjectTerms | Clustered Multi-Task Learning Covariance matrices Group Sparsity Kernel Learning systems Optimization Representative Task Robustness Training data Visualization |
| Title | Flexible Clustered Multi-Task Learning by Learning Representative Tasks |
| URI | https://ieeexplore.ieee.org/document/7150415 https://www.ncbi.nlm.nih.gov/pubmed/26761733 https://www.proquest.com/docview/1760871727 |
| Volume | 38 |
| WOSCitedRecordID | wos000369989600006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 2160-9292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014503 issn: 0162-8828 databaseCode: RIE dateStart: 19790101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8QwEB5UPOjBVdfH-qKCN602Tdu0R1lc9aCIrLC3ksesiMuu7Av8907ShwoqeMshSdvMJPmmM_MNwAkh2FTFKvZ5n6EfZZGlvI3oMJRaZpYvXIXKFZsQ9_dpr5c9LMBZnQuDiC74DM9t0_nyzUjP7K-yC0HoxWWULwohilyt2mMQxa4KMiEY2uFkRlQJMkF20X24vLu1UVzxufMzMlseJkzIgBecf7uPXIGV37Gmu3M6jf-97TqsldjSuyyUYQMWcLgJjapug1du401Y_UJC2ITrjuXEVAP02oOZpU1A47m0XL8rJ69eScD67Kn3z_aji58t0pbm6NmOky146lx12zd-WV7B1zzkU9-kTAeqL-nMYobsFtY3kgxEHkmmBNexRKEyQmiKLKZMBwmGymiUaMimDGXC-TYsDUdD3AWvLyKCv7GmyVQUM54qkn-aoJE6CZk2LWDVIue65B63JTAGubNBgix3MsqtjPJSRi04rce8Fcwbf_ZuWgnUPcvFb8FxJcuc9o11hsghjmaTnIkkoG8m-NaCnULI9eBKN_Z-nnQfVujRZez2ASxNxzM8hGU9n75MxkeknL30yCnnByI33OU |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1ZSwMxEB5EBfXB-6jnCr7p1s0mez2KWBW1iFTwbckxFbG0YlvBf-8ke6iggm95SEI2M0m-2Zn5BuCAEGyqIhX5vMvQF5mwlLeCLkOpZWb5wlWoXLGJpN1OHx6y2wk4qnNhENEFn2HTNp0v3wz02P4qO04IvbiM8qlIiJAV2Vq1z0BErg4yYRg642RIVCkyQXbcuT25ubRxXFHTeRqZLRATxmTCJ5x_e5FciZXf0aZ7dVoL_1vvIsyX6NI7KdRhCSawvwwLVeUGrzzIyzD3hYZwBc5blhVT9dA77Y0tcQIazyXm-h05fPZKCtZHT71_tu9cBG2RuPSGnu04XIX71lnn9MIvCyz4mod85JuU6UB1Jd1azJDlwrpGkonIhWQq4TqSmKiMMJoimynTQYyhMholGrIqQxlzvgaT_UEfN8DrJoIAcKRpMiUixlNFGpDGaKSOQ6ZNA1i1ybku2cdtEYxe7qyQIMudjHIro7yUUQMO6zEvBffGn71XrATqnuXmN2C_kmVOJ8e6Q2QfB-NhzpI4oG8mANeA9ULI9eBKNzZ_nnQPZi46N9f59WX7agtmaRllJPc2TI5ex7gD0_pt9DR83XUq-gGS299E |
| 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=Flexible+Clustered+Multi-Task+Learning+by+Learning+Representative+Tasks&rft.jtitle=IEEE+transactions+on+pattern+analysis+and+machine+intelligence&rft.au=Zhou%2C+Qiang&rft.au=Zhao%2C+Qi&rft.date=2016-02-01&rft.issn=0162-8828&rft.eissn=2160-9292&rft.volume=38&rft.issue=2&rft.spage=266&rft.epage=278&rft_id=info:doi/10.1109%2FTPAMI.2015.2452911&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TPAMI_2015_2452911 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0162-8828&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0162-8828&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0162-8828&client=summon |