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TenLa: an approach based on controllable tensor decomposition and optimized lasso regression for judgement prediction of legal cases.

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Title: TenLa: an approach based on controllable tensor decomposition and optimized lasso regression for judgement prediction of legal cases.
Authors: Guo, Xiaoding1, 15b903068@hit.edu.cn, Zhang, Hongli1, Ye, Lin1, Li, Shang1
Source: Applied Intelligence; Apr2021, Vol. 51 Issue 4, p2233-2252, 20p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 149714944
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: TenLa: an approach based on controllable tensor decomposition and optimized lasso regression for judgement prediction of legal cases.
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  Data: <searchLink fieldCode="AU" term="%22Guo%2C+Xiaoding%22">Guo, Xiaoding</searchLink><relatesTo>1</relatesTo>, <i>15b903068@hit.edu.cn</i><br /><searchLink fieldCode="AU" term="%22Zhang%2C+Hongli%22">Zhang, Hongli</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AU" term="%22Ye%2C+Lin%22">Ye, Lin</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AU" term="%22Li%2C+Shang%22">Li, Shang</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Applied+Intelligence%22">Applied Intelligence</searchLink>; Apr2021, Vol. 51 Issue 4, p2233-2252, 20p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=149714944
RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1007/s10489-020-01912-z
    Languages:
      – Code: eng
        Text: English
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        PageCount: 20
        StartPage: 2233
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      – TitleFull: TenLa: an approach based on controllable tensor decomposition and optimized lasso regression for judgement prediction of legal cases.
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            NameFull: Guo, Xiaoding
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            NameFull: Zhang, Hongli
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            NameFull: Ye, Lin
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            NameFull: Li, Shang
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            – D: 01
              M: 04
              Text: Apr2021
              Type: published
              Y: 2021
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              Value: 0924669X
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              Value: 51
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Applied Intelligence
              Type: main
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