A Few Useful Things to Know About Machine Learning.

The author discusses machine learning, also known as predictive analytics or data mining, focusing on lessons for computer science researchers and the use of algorithms to perform computational tasks with more efficiency than manual computer programming. The author argues that all machine learning h...

Full description

Bibliographic Details
Published in:Communications of the ACM Vol. 55; no. 10; pp. 78 - 88
Main Author: Domingos, Pedro
Format: Article
Published: Association for Computing Machinery Oct2012
Subjects:
Online Access:View this record in EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=82151052&site=ehost-live
header:
  @attributes:
    shortDbName: hlh
    uiTerm: 82151052
    longDbName: Humanities International Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    jinfo:
      jid:
        00010782
        ACM
      jtl: Communications of the ACM
      issn: 00010782
      maglogo: N
    pubinfo:
      dt: Oct2012
      vid: 55
      iid: 10
      pid: 68
      pub: Association for Computing Machinery
    artinfo:
      ui:
        82151052
        10.1145/2347736.2347755
      ppf: 78
      ppct: 10
      formats:
      tig:
        atl: A Few Useful Things to Know About Machine Learning.
      aug:
        au: Domingos, Pedro
        affil: Professor, Department of Computer Science and Engineering, University of Washington, Seattle
      su:
        Machine learning
        Machine theory
        Computer science
        Algorithms
        Analysis of variance
        Data mining
      sug:
        subj:
          Machine learning
          Machine theory
          Computer science
          Algorithms
          Analysis of variance
          Data mining
      ab: The author discusses machine learning, also known as predictive analytics or data mining, focusing on lessons for computer science researchers and the use of algorithms to perform computational tasks with more efficiency than manual computer programming. The author argues that all machine learning has to do with representation, evaluation, and optimization. Topics include decision trees, probabilistic guarantees, overfitting, and bias and variance in generalization error. Bayesian model averaging (BMA), scalability, and Boolean domains are mentioned.
      pubtype: Periodical
      doctype: Article
      src: R
    language: English
    refInfo:
    copyright:
      @attributes:
        flag: Y
      dt:
        @attributes:
          year: 2012
    holdings:
      @attributes:
        islocal: N