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[null,"1.","Record Nr.",null,"UNINA9910629297203321"]
[null,null,"Autore",null,"Fieguth Paul"]
[null,null,"Titolo",null,"An Introduction to Pattern Recognition and Machine Learning / / by Paul Fieguth"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022"]
[null,null,"ISBN",null,"9783030959951"]
[{"$nulls":4},"9783030959937"]
[null,null,"Edizione",null,"[1st ed. 2022.]"]
[null,null,"Descrizione fisica",null,"1 online resource (481 pages)"]
[null,null,"Disciplina",null,"006.31"]
[{"$nulls":4},"006.4"]
[null,null,"Soggetti",null,"Signal processing"]
[{"$nulls":4},"Pattern recognition systems"]
[{"$nulls":4},"System theory"]
[{"$nulls":4},"Data mining"]
[{"$nulls":4},"Digital and Analog Signal Processing"]
[{"$nulls":4},"Automated Pattern Recognition"]
[{"$nulls":4},"Complex Systems"]
[{"$nulls":4},"Data Mining and Knowledge Discovery"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Chapter 1. Overview -- Chapter 2. Introduction to Pattern Recognition -- Chapter 3. Learning -- Chapter 4. Representing Patterns -- Chapter 5. Feature Extraction and Selection -- Chapter 6. Distance-Based Classification -- Chapter 7. Inferring Class Models -- Chapter 8. Statistics-Based Classification -- Chapter 9. Classifier Testing and Validation -- Chapter 10. Discriminant-Based Classification -- Chapter 11. Ensemble Classification -- Chapter 12. Model-Free Classification -- Chapter 13. Conclusions and Directions."]
[null,null,null,"Sommario/riassunto","The domains of Pattern Recognition and Machine Learning have experienced exceptional interest and growth, however the overwhelming number of methods and applications can make the fields seem bewildering. This text offers an accessible and conceptually rich introduction, a solid mathematical development emphasizing simplicity and intuition. Students beginning to explore pattern recognition do not need a suite of mathematically advanced methods or complicated "]
[{"$nulls":4},"computational libraries to understand and appreciate pattern recognition; rather the fundamental concepts and insights, eminently teachable at the undergraduate level, motivate this text. This book provides methods of analysis that the reader can realistically undertake on their own, supported by real-world examples, case-studies, and worked numerical / computational studies."]