LEADER 01484nam2-2200409li-450 001 990000204810203316 005 20180312154623.0 010 $a3-540-56393-8 035 $a0020481 035 $aUSA010020481 035 $a(ALEPH)000020481USA01 035 $a0020481 100 $a20001109d1993----km-y0itay0103----ba 101 0 $aeng 102 $aGW 200 1 $aConditional term rewriting systems$ethird International workshop, CTRS'92$ePont-à-Mousson, France, July 8-10, 1992$eproceedings$fM. Rusinowitch, (ed.) 210 $aBerlin [etc.]$cSpringer-Verlag$dcopyr. 1993 215 $aXI, 501 p.$cill.$d24 cm 225 2 $aLecture notes in computer science$v656 410 0$10010020264$12001$aLecture notes in computer science 610 1 $acongressi$apont-a-mousson$a1992 610 1 $agrafica con gli elaboratori$acongressi$a1992 610 1 $alinguaggi di programmazione$acongressi$a1992 676 $a0066$9Grafica con gli eleboratori 702 1$aRusinowitch,$bMichaël 710 12$aInternational workshop CTRS-92$d3.$ePont-á-Mousson$f1992$0745739 801 $aSistema bibliotecario di Ateneo dell' Università di Salerno$gRICA 912 $a990000204810203316 951 $a001 LNCS (656)$b0015101 959 $aBK 969 $aSCI 979 $c19941201 979 $c20001110$lUSA01$h1714 979 $c20020403$lUSA01$h1628 979 $aPATRY$b90$c20040406$lUSA01$h1615 996 $aConditional term rewriting systems$91487601 997 $aUNISA LEADER 03768nam 22005895 450 001 9910831001503321 005 20240205053338.0 010 $a9789819970070 010 $a9819970075 024 7 $a10.1007/978-981-99-7007-0 035 $a(MiAaPQ)EBC31124763 035 $a(Au-PeEL)EBL31124763 035 $a(MiAaPQ)EBC31132796 035 $a(Au-PeEL)EBL31132796 035 $a(OCoLC)1420629919 035 $a(DE-He213)978-981-99-7007-0 035 $a(CKB)30305728500041 035 $a(EXLCZ)9930305728500041 100 $a20240205d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aOnline Machine Learning $eA Practical Guide with Examples in Python /$fedited by Eva Bartz, Thomas Bartz-Beielstein 205 $a1st ed. 2024. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024. 215 $a1 online resource (163 pages) 225 1 $aMachine Learning: Foundations, Methodologies, and Applications,$x2730-9916 311 08$a9789819970063 311 08$a9819970067 327 $aChapter 1:Introduction -- Chapter 2:Supervised Learning -- Chapter 3:Drift Detection and Handling -- Chapter 4:Initial Selection and Subsequent Updating of OML Models -- Chapter 5:Evaluation and Performance Measurement -- Chapter 6:Special Requirements for OML Methods -- Chapter 7:Practical Applications of Online Machine Learning -- Chapter 8:Open-Source-Software for Online Machine Learning -- Chapter 9:An Experimental Comparison of Batch and Online Machine Learning Algorithms -- Chapter 10:Hyperparameter Tuning -- Chapter 11:Summary and Outlook. 330 $aThis book deals with the exciting, seminal topic of Online Machine Learning (OML). The content is divided into three parts: the first part looks in detail at the theoretical foundations of OML, comparing it to Batch Machine Learning (BML) and discussing what criteria should be developed for a meaningful comparison. The second part provides practical considerations, and the third part substantiates them with concrete practical applications. The book is equally suitable as a reference manual for experts dealing with OML, as a textbook for beginners who want to deal with OML, and as a scientific publication for scientists dealing with OML since it reflects the latest state of research. But it can also serve as quasi OML consulting since decision-makers and practitioners can use the explanations to tailor OML to their needs and use it for their application and ask whether the benefits of OML might outweigh the costs. OML will soon become practical; it is worthwhile to get involved with it now. This book already presents some tools that will facilitate the practice of OML in the future. A promising breakthrough is expected because practice shows that due to the large amounts of data that accumulate, the previous BML is no longer sufficient. OML is the solution to evaluate and process data streams in real-time and deliver results that are relevant for practice. 410 0$aMachine Learning: Foundations, Methodologies, and Applications,$x2730-9916 606 $aArtificial intelligence 606 $aMachine learning 606 $aArtificial Intelligence 606 $aMachine Learning 615 0$aArtificial intelligence. 615 0$aMachine learning. 615 14$aArtificial Intelligence. 615 24$aMachine Learning. 676 $a006.3 700 $aBartz$b Eva$01423767 701 $aBartz-Beielstein$b Thomas$01337543 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910831001503321 996 $aOnline Machine Learning$94048684 997 $aUNINA