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  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Evaluating learning algorithms
    sub title: a classification perspective
  name:
    name part: Japkowicz, Nathalie.
    role:
      role term: creator
  name:
    name part: Shah, Mohak.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: enk
    date issued: 2011
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: print
    extent: xvi, 406 pages : illustrations ; 24 cm.
  abstract: The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines various aspects of the evaluation process with an emphasis on classification algorithms. The authors describe several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. They also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. The techniques presented in the book are illustrated using R and WEKA facilitating better practical insight as well as implementation. Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for conducting performance evaluations of algorithms in practical settings.
  abstract: Technological advances, in recent decades, have made it possible to automate many tasks that previously required signi.cant amounts of manual time, performing regular or repetitive activities. Certainly, computing machines have proven to be a great asset in improving on human speed and e.ciency as well as in reducing errors in these essentially mechanical tasks. More impressively, however, the emergence of computing technologies has also enabled the automation of tasks that require signi.cant understanding of intrinsically human domains that can, in no way, be qualified as merely mechanical. While we, humans, have maintained an edge in performing some of these tasks, e.g. recognizing pictures or delineating boundaries in a given picture, we have been less successful at others, e.g., fraud or computer network attack detection, owing to the sheer volume of data involved, and to the presence of nonlinear patterns to be discerned and analyzed simultaneously within these data. Machine Learning and Data Mining, on the other hand, have heralded significant advances, both theoretical and applied, in this direction, thus getting us one step closer to realizing such goals.
  table of contents: 1. Introduction; 2. Machine learning and statistics overview; 3. Performance measures I; 4. Performance measures II; 5. Error estimation; 6. Statistical significance testing; 7. Data sets and experimental framework; 8. Recent developments; 9. Conclusion; Appendix A: statistical tables; Appendix B: additional information on the data; Appendix C: two case studies.
  note: Nathalie Japkowicz, Mohak Shah.
  note: Includes bibliographical references and index.
  subject:
    topic: Machine learning
  subject:
    topic: Computer algorithms
    topic: Evaluation
  classification: Q325.5 .J37 2011
  classification: 006.3/1
  identifier: 9780521196000
  identifier: 0521196000
  identifier: 2010048733
  identifier: http://catdir.loc.gov/catdir/enhancements/fy1102/2010048733-b.html
  identifier: http://catdir.loc.gov/catdir/enhancements/fy1102/2010048733-d.html
  identifier: http://catdir.loc.gov/catdir/enhancements/fy1102/2010048733-t.html
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  record info:
    record content source: DLC
    record creation date: 101116
    record change date: 20151202114954.0
    record identifier: in00002669553