rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Fundamentals of Pattern Recognition and Machine Learning
  name:
    name part: Braga-Neto, Ulisses.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: sz
    date issued: 2020
    edition: 1st ed. 2020.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (XVIII, 357 pages 84 illustrations, 73 illustrations in color.)
  abstract: Fundamentals of Pattern Recognition and Machine Learning is designed for a one or two-semester introductory course in Pattern Recognition or Machine Learning at the graduate or advanced undergraduate level. The book combines theory and practice and is suitable to the classroom and self-study. The book is intended to be concise but thorough. It does not attempt an encyclopedic approach, but covers in significant detail the tools commonly used in pattern recognition and machine learning, including classification, dimensionality reduction, regression, and clustering, as well as recent popular topics such as Gaussian process regression and convolutional neural networks. In addition, the selection of topics has a few features that are unique among comparable texts: it contains an extensive chapter on classifier error estimation, as well as sections on Bayesian classification, Bayesian error estimation, separate sampling, and rank-based classification. The book is mathematically rigorous and covers the classical theorems in the area. Nevertheless, an effort is made in the book to strike a balance between theory and practice. In particular, examples with datasets from applications in bioinformatics and materials informatics are used throughout to illustrate the theory. These datasets are available from the book website to be used in end-of-chapter coding assignments based on python and scikit-learn. All plots in the text were generated using python scripts, which are also available on the book website.
  table of contents: 1. Introduction -- 2. Optimal Classification -- 3. Sample-Based Classification -- 4. Parametric Classification -- 5. Nonparametric Classification -- 6. Function-Approximation Classification -- 7. Error Estimation for Classification -- 8. Model Selection for Classification -- 9. Dimensionality Reduction -- 10. Clustering -- 11. Regression -- Appendix.
  note: by Ulisses Braga-Neto.
  subject:
    topic: Pattern recognition
  subject:
    topic: Optical data processing
  subject:
    topic: Probabilities
  subject:
    topic: Pattern Recognition
  subject:
    topic: Image Processing and Computer Vision
  subject:
    topic: Probability Theory and Stochastic Processes
  classification: Q337.5
  classification: TK7882.P3
  classification: 006.4
  related item:
    title info:
      title: Springer Nature eBook
  identifier: 9783030276560
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record creation date: 200910
    record change date: 20230330184901.3
    record identifier: in00004383720