View in EDS PDF Full Text

Towards a Theory for Designing Machine Learning Systems for Complex Decision Making Problems

Saved in:
Bibliographic Details
Title: Towards a Theory for Designing Machine Learning Systems for Complex Decision Making Problems
Description: The ubiquitousness of data and the emergence of data-driven machine learning approaches provide new means of creating insights. However, coping with the great volume, velocity, and variety of data requires improved data analysis methods. This dissertation contributes a nascent design theory, named the Division-of-Labor framework, for developing complex machine learning systems that can not only address the challenges of big data but also leverage their characteristics to perform more sophisticated analyses. I evaluate the proposed design principles in three practical settings, in which I apply the principles to design machine learning systems that (i) support treatment decision making for cancer patients, (ii) provide consumers with recommendations on two-sided platforms, and (iii) address a trade-off between efficiency and comfort in the context of autonomous vehicles. The evaluations partially validate the proposed theory, but also show that some principles require further attention in order to be practicable.
Authors: Tofangchi, Schahin
Resource Type: eBook.
Subjects: Machine learning
Categories: COMPUTERS / Machine Theory, COMPUTERS / Artificial Intelligence / Expert Systems
Database: eBook Collection (EBSCOhost)
FullText Links:
  – Type: ebook-pdf
Text:
  Availability: 0
Header DbId: nlebk
DbLabel: eBook Collection (EBSCOhost)
An: 2457002
RelevancyScore: 1097
AccessLevel: 6
PubType: eBook
PubTypeId: ebook
PreciseRelevancyScore: 1096.64697265625
IllustrationInfo
ImageInfo – Size: thumb
  Target: https://rps2images.ebscohost.com/rpsweb/othumb?id=NL$2457002$PDF&s=r
– Size: medium
  Target: https://rps2images.ebscohost.com/rpsweb/othumb?id=NL$2457002$PDF&s=d
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Towards a Theory for Designing Machine Learning Systems for Complex Decision Making Problems
– Name: Abstract
  Label: Description
  Group: Ab
  Data: The ubiquitousness of data and the emergence of data-driven machine learning approaches provide new means of creating insights. However, coping with the great volume, velocity, and variety of data requires improved data analysis methods. This dissertation contributes a nascent design theory, named the Division-of-Labor framework, for developing complex machine learning systems that can not only address the challenges of big data but also leverage their characteristics to perform more sophisticated analyses. I evaluate the proposed design principles in three practical settings, in which I apply the principles to design machine learning systems that (i) support treatment decision making for cancer patients, (ii) provide consumers with recommendations on two-sided platforms, and (iii) address a trade-off between efficiency and comfort in the context of autonomous vehicles. The evaluations partially validate the proposed theory, but also show that some principles require further attention in order to be practicable.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Tofangchi%2C+Schahin%22">Tofangchi, Schahin</searchLink>
– Name: TypePub
  Label: Resource Type
  Group: TypPub
  Data: eBook.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink>
– Name: SubjectBISAC
  Label: Categories
  Group: Su
  Data: <searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Machine+Theory%22">COMPUTERS / Machine Theory</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Artificial+Intelligence+%2F+Expert+Systems%22">COMPUTERS / Artificial Intelligence / Expert Systems</searchLink>
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=2457002
RecordInfo BibRecord:
  BibEntity:
    Classifications:
      – Code: 006.31
        Scheme: ddc
        Type: prePub
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Machine learning
        Type: general
    Titles:
      – TitleFull: Towards a Theory for Designing Machine Learning Systems for Complex Decision Making Problems
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Tofangchi, Schahin
      – PersonEntity:
          Name:
            NameFull: Tofangchi, Schahin
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2020
            – D: 08
              M: 12
              Type: profile
              Y: 2022
          Identifiers:
            – Type: isbn-print
              Value: 9783736972001
            – Type: isbn-electronic
              Value: 9783736962002
          Numbering:
            – Type: volume
              Value: 00101
          Titles:
            – TitleFull: Towards a Theory for Designing Machine Learning Systems for Complex Decision Making Problems
              Type: main
ResultId 1