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010   $a9783030959951$b(electronic bk.)
010   $z9783030959937
024 7 $a10.1007/978-3-030-95995-1
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035   $a(OCoLC)1350666906
035   $a(BIP)86353805
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035   $a(DE-He213)978-3-030-95995-1
035   $a(EXLCZ)9925299477300041
100   $a20221109d2022      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 13$aAn Introduction to Pattern Recognition and Machine Learning /$fby Paul Fieguth
205   $a1st ed. 2022.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022.
215   $a1 online resource (481 pages)
311 08$aPrint version: Fieguth, Paul An Introduction to Pattern Recognition and Machine Learning Cham : Springer International Publishing AG,c2022 9783030959937 
320   $aIncludes bibliographical references and index.
327   $aChapter 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.
330   $aThe 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 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.
606   $aSignal processing
606   $aPattern recognition systems
606   $aSystem theory
606   $aData mining
606   $aDigital and Analog Signal Processing
606   $aAutomated Pattern Recognition
606   $aComplex Systems
606   $aData Mining and Knowledge Discovery
615  0$aSignal processing.
615  0$aPattern recognition systems.
615  0$aSystem theory.
615  0$aData mining.
615 14$aDigital and Analog Signal Processing.
615 24$aAutomated Pattern Recognition.
615 24$aComplex Systems.
615 24$aData Mining and Knowledge Discovery.
676   $a006.31
676   $a006.4
700   $aFieguth$b Paul$0818349
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
912   $a9910629297203321
996   $aIntroduction to Pattern Recognition and Machine Learning$94161072
997   $aUNINA