Introduction to statistical pattern recognition / Keinosuke Fukunaga.
Material type:
TextSeries: Computer science and scientific computingPublication details: Boston : Academic Press, ©1990. Edition: 2nd edDescription: 1 online resource (xiii, 591 pages) : illustrationsContent type: text Media type: computer Carrier type: online resourceISBN: 9780080478654; 0080478654; 0122698517; 9780122698514Subject(s): Pattern perception -- Statistical methods | Decision making -- Mathematical models | Mathematical statistics | Prise de décision -- Modèles mathématiques | Perception des structures -- Méthodes statistiques | Statistique mathématique | COMPUTERS -- Optical Data Processing | Decision making -- Mathematical models | Mathematical statistics | Pattern perception -- Statistical methods | Statistiek | Patroonherkenning | Perception des structures | Décision, prise de -- Modèles mathématiques | Statistique mathématique | Pattern recognitionGenre/Form: Electronic books. | Electronic books. Additional physical formats: Print version:: Introduction to statistical pattern recognition.DDC classification: 006.4 LOC classification: Q327 | .F85 1990ebOnline resources: Click here to access online | Item type | Current library | Collection | Call number | Status | Date due | Barcode | Item holds |
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Includes bibliographical references and index.
Print version record.
Cover; Frontmatter; Chapter 1: Introduction; Chapter 2: Random Vectors and Their Properties; Chapter 3: Hypothesis Testing; Chapter 4: Parametric Classifiers; Chapter 5: Parameter Estimation; Chapter 6: Nonparametric Density Estimation; Chapter 7: Nonparametric Classification and Error Estimation; Chapter 8: Successive Parameter Estimation; Chapter 9: Feature Extraction and Linear Mapping for Signal Representation; Chapter 10: Feature Extraction and Linear Mapping for Classification; Chapter 11: Clustering; Backmatter; Back Cover.
This revised second edition presents an introduction to statistical pattern recognition. Pattern recognition in general covers a range of problems: it is applied to engineering problems, such as character readers and wave form analysis as well as to brain modeling in biology and psychology.