Quantum-inspired learning vector quantizers for prototype-based classification: Confidential: for personal use only—submitted to Neural Networks and Applications 5/2020.

Saved in:
Bibliographic Details
Title: Quantum-inspired learning vector quantizers for prototype-based classification: Confidential: for personal use only—submitted to Neural Networks and Applications 5/2020.
Authors: Villmann, Thomas1, thomas.villmann@hs-mittweida.de, Engelsberger, Alexander1, Ravichandran, Jensun1, Villmann, Andrea2, Kaden, Marika1
Source: Neural Computing & Applications; Jan2022, Vol. 34 Issue 1, p79-88, 10p
Database: Applied Science & Technology Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 154580928
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Quantum-inspired learning vector quantizers for prototype-based classification: Confidential: for personal use only—submitted to Neural Networks and Applications 5/2020.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AU" term="%22Villmann%2C+Thomas%22">Villmann, Thomas</searchLink><relatesTo>1</relatesTo>, <i>thomas.villmann@hs-mittweida.de</i><br /><searchLink fieldCode="AU" term="%22Engelsberger%2C+Alexander%22">Engelsberger, Alexander</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AU" term="%22Ravichandran%2C+Jensun%22">Ravichandran, Jensun</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AU" term="%22Villmann%2C+Andrea%22">Villmann, Andrea</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AU" term="%22Kaden%2C+Marika%22">Kaden, Marika</searchLink><relatesTo>1</relatesTo>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>; Jan2022, Vol. 34 Issue 1, p79-88, 10p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=154580928
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s00521-020-05517-y
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 10
        StartPage: 79
    Titles:
      – TitleFull: Quantum-inspired learning vector quantizers for prototype-based classification: Confidential: for personal use only—submitted to Neural Networks and Applications 5/2020.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Villmann, Thomas
      – PersonEntity:
          Name:
            NameFull: Engelsberger, Alexander
      – PersonEntity:
          Name:
            NameFull: Ravichandran, Jensun
      – PersonEntity:
          Name:
            NameFull: Villmann, Andrea
      – PersonEntity:
          Name:
            NameFull: Kaden, Marika
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Text: Jan2022
              Type: published
              Y: 2022
          Identifiers:
            – Type: issn-print
              Value: 09410643
          Numbering:
            – Type: volume
              Value: 34
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Neural Computing & Applications
              Type: main
ResultId 1