00866nam0-22003131i-450-99000321747040332120110120174902.0000321747FED01000321747(Aleph)000321747FED0100032174720030910d1951----km-y0itay50------baitaIT<<La >>teoria della cultura e l'insegnamentoRaffaele Resta.GenovaIstituto Universitario di Magistero\[stampa \\1951].V, 383 p.22 cmCulturaPedagogia1312013990Resta,Raffaele<1876-1961>ITUNINARICAUNIMARCBK99000321747040332113120 RES04559SESSESTeoria della cultura e l'insegnamento455470UNINA00964cam0 2200277 450 E60020000265720230711105028.020040526d1964 |||||ita|0103 baitaITEsteban Bartolomè Murillo[cur.] Raffaello CausaMilanoFabbri1964[6] p.ill., XVI tav.36 cm<I >maestri del colore51001LAEC000202162001 I *maestri del colore51Murillo, Bartolomé EstebanAF00011773070475968Causa, RaffaelloAF00009844070ITUNISOB20230711RICAUNISOBUNISOB700|Coll|48|K84220E600200002657M 102 Monografia moderna SBNM700|Coll|48|K000051SI84220acquistopregresso1UNISOBUNISOB20040526091930.020230711105028.0SpinosaEsteban Bartolomè Murillo1673797UNISOB03605nam 22006855 450 991062929720332120251202151913.09783030959951(electronic bk.)978303095993710.1007/978-3-030-95995-1(MiAaPQ)EBC7134132(Au-PeEL)EBL7134132(CKB)25299477300041(PPN)266353738(MiAaPQ)EBC31888900(Au-PeEL)EBL31888900(OCoLC)1350666906(BIP)86353805(BIP)82787716(DE-He213)978-3-030-95995-1(EXLCZ)992529947730004120221109d2022 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierAn Introduction to Pattern Recognition and Machine Learning /by Paul Fieguth1st ed. 2022.Cham :Springer International Publishing :Imprint: Springer,2022.1 online resource (481 pages)Print version: Fieguth, Paul An Introduction to Pattern Recognition and Machine Learning Cham : Springer International Publishing AG,c2022 9783030959937 Includes bibliographical references and index.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.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 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.Signal processingPattern recognition systemsSystem theoryData miningDigital and Analog Signal ProcessingAutomated Pattern RecognitionComplex SystemsData Mining and Knowledge DiscoverySignal processing.Pattern recognition systems.System theory.Data mining.Digital and Analog Signal Processing.Automated Pattern Recognition.Complex Systems.Data Mining and Knowledge Discovery.006.31006.4Fieguth Paul818349MiAaPQMiAaPQMiAaPQ9910629297203321Introduction to Pattern Recognition and Machine Learning4161072UNINA