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: Pattern recognition and machine learning
  title info:
    title: Ninshiki to gakushū. English
  name:
    name part: Anzai, Yūichirō
    name part: 1946-
    role:
      role term: creator
  name:
    name part: ScienceDirect (Online service)
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: mau
    place:
      place term: Boston
    publisher: Academic Press
    date issued: ©1992
    date issued: 1992
    issuance: monographic
  language:
    language term: eng
  language:
    language term: jpn
  physical description:
    form: remote
    extent: 1 online resource (xvi, 407 pages) : illustrations
  abstract: This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.
  table of contents: Front cover; Pattern Recognition and Machine Learning; Copyright page; Tabel of Contents; Preface; Study Guide; Chapter 1. Recognition and Learning by a Computer; 1.1 What Is Recognition by a Computer?; 1.2 Representation and Transformationin Recognition; 1.3 What Is Learning by a Computer?; 1.4 Representation and Transformationin Learning; 1.5 Example of Recognition/Learning System; Summary; Keywords; Exercises; Chapter 2. Representing Information; 2.1 Pattern Function and Bit Pattern; 2.2 The Representation of Spatial Structure; 2.3 Graph Representation; 2.4 Tree Representation.
  table of contents: 2.5 List Representation2.6 Predicate Logic Representation; 2.7 Horn Clause Logic Representation; 2.8 Declarative Representation; 2.9 Procedural Representation; 2.10 Representation Using Rules; 2.11 Semantic Networks and Frames; 2.12 Representation Using Fourier Series; 2.13 Classification of Representation Methods; Summary; Keywords; Exercises; Chapter 3. Generation and Transformation of Representations; 3.1 Methods of Generating and Transforming Representations; 3.2 Linear Transformations of Pattern Functions; 3.3 Sampling and Quantization of Pattern Functions.
  table of contents: 3.4 Transformation to Spatial Representations3.5 Generation of Tree Representation; 3.6 Search and Problem Solving; 3.7 Logical Inference; 3.8 Production Systems; 3.9 Inference Using Frames; 3.10 Constraint Representation and Relaxation; 3.11 Summary; Keywords; Exercises; Chapter 4. Pattern Feature Extraction; 4.1 Detecting an Edge; 4.2 Detection of a Boundary Line; 4.3 Extracting a Region; 4.4 Texture Analysis; 4.5 Detection of Movement; 4.6 Representing a Boundary Line; 4.7 Representing a Region; 4.8 Representation of a Solid; 4.9 Interpretation of Line Drawings; Summary; Keywords.
  table of contents: ExercisesChapter 5. Pattern UnderstandingMethods; 5.1 Pattern Understanding and Knowledge Representation; 5.2 Pattern Matching and the Relaxation Method; 5.3 Maximal Subgraph Isomorphism and Clique Method; 5.4 Control in Pattern Understanding; Summary; Keywords; Exercises; Chapter 6. Learning Concepts; 6.1 Definition of a Concept; 6.2 Methods for Concept Learning; 6.3 Generalization of Well-Formed Formulas; 6.4 Version Space; 6.5 Conceptual Clustering; Summary; Keywords; Exercises; Chapter 7. Learning Procedures; 7.1 Learning Operators in Problem Solving; 7.2 Learning Rules.
  table of contents: 7.3 Learning ProgramsSummary; Keywords; Exercises; Chapter 8. Learning Based on Logic; 8.1 Explanation-Based Learning; 8.2 Analogical Learning; 8.3 Nonmonotonic Logic and Learning; Summary; Keywords; Exercises; Chapter 9. Learning by Classification and Discovery; 9.1 Representing Instances by a Decision Tree; 9.2 An Algorithm for Generating a Decision Tree; 9.3 Selecting a Test in Generating a Decision Tree; 9.4 Learning from Noisy Data; 9.5 Learning by Discovery; 9.6 Discovery of New Concepts and Rules; Summary; Keywords; Exercises; Chapter 10. Learning by Neural Networks.
  note: Yuichiro Anzai.
  note: Includes bibliographical references (pages 387-402) and index.
  subject:
    topic: Pattern perception
  subject:
    topic: Machine learning
  subject:
    topic: Perception des structures
  subject:
    topic: Apprentissage automatique
  subject:
    topic: COMPUTERS
    topic: Computer Vision & Pattern Recognition
  subject:
    topic: Machine learning
  subject:
    topic: Pattern perception
  subject:
    topic: RECONHECIMENTO DE PADRÕES
  subject:
    topic: APRENDIZADO COMPUTACIONAL
  subject:
    topic: Perception des structures
  subject:
    topic: Reconnaissance des formes (informatique)
  subject:
    topic: Apprentissage automatique
  classification: Q327 .A5813 1992eb
  classification: 006.3/7
  classification: 54.72
  classification: *68T10
  classification: 68-01
  classification: 68T05
  classification: 68T30
  classification: 68U10
  related item:
    title info:
      title: Pattern recognition and machine learning
    title info:
      title: Ninshiki to gakushū. English
    name:
      name part: Anzai, Yūichirō, 1946-
    origin info:
      publisher: Boston : Academic Press, ©1992
    identifier: (DLC) 92007073
    identifier: (OCoLC)25410182
  identifier: 9780080513638
  identifier: 0080513638
  identifier: 0120588307
  identifier: 9780120588305
  identifier: 92007073
  identifier: CL0500000427 Safari Books Online
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: OPELS
    record creation date: 130411
    record change date: 20260326194325.3
    record identifier: in00005751940
    language of cataloging:
      language term: eng