LEADER 01084nam--2200373---450- 001 990002082120203316 005 20090406113731.0 035 $a000208212 035 $aUSA01000208212 035 $a(ALEPH)000208212USA01 035 $a000208212 100 $a20041014d1968----km-y0itay0103----ba 101 0 $aeng 102 $aGB 105 $a||||||||001yy 200 1 $aEnglish agriculture in 1850-51$fJames Caird$gwith a new introduction by G. E. Mingay 210 $aLondon$cFrank Cass & Co.$d1968 215 $aLII, 548 p.$d23 cm 410 0$12001 454 1$12001 461 1$1001-------$12001 676 $a630 700 1$aCAIRD,$bJames$0136019 702 1$aMINGAY,$bGordon Edmund 801 0$aIT$bsalbc$gISBD 912 $a990002082120203316 951 $a630 CAI 1 (ISE VI 56)$b12307 E.C.$cISE VI$d00204757 959 $aBK 969 $aECO 979 $aSIAV4$b10$c20041014$lUSA01$h1158 979 $aRSIAV2$b90$c20090406$lUSA01$h1137 979 $aRSIAV2$b90$c20090406$lUSA01$h1137 996 $aEnglish agriculture in 1850-51$9492085 997 $aUNISA LEADER 01551nam 2200325Ia 450 001 996387252603316 005 20200824132422.0 035 $a(CKB)4940000000086783 035 $a(EEBO)2240901135 035 $a(OCoLC)ocm49521028e 035 $a(OCoLC)49521028 035 $a(EXLCZ)994940000000086783 100 $a20020404d1674 uy 0 101 0 $aeng 135 $aurbn||||a|bb| 200 10$aPhysick for families: or, The new, safe, and powerfull way of physick, upon constant proof established;$b[electronic resource] $eenabling every one, at sea or land, by the medicines herein mentioned, to cure themselves, their friends, and relations, in all distempers and diseases. : Without any the trouble, hazzard, pain, or danger of purgers, vomitters, bleedings, issues, glisters, blisters, opium, antimony, and quicksilver, so full of perplexity in sickness. /$fBy W.W. .. 210 $aLondon, $cPrinted, by J.R. and are to be sold by Robert Horn ...$d1674 215 $a109 p 300 $aImperfect: p. 97-98 lacking. Tight binding with some loss of print. 300 $aReproduction of original in: Wellcome Institute for the History of Medicine. Library. 330 $aeebo-0186 606 $aMedicine$y15th-18th centuries 615 0$aMedicine 700 $aWalwyn$b William$f1600-1681.$01002660 801 0$bEAE 801 1$bEAE 906 $aBOOK 912 $a996387252603316 996 $aPhysick for families: or, The new, safe, and powerfull way of physick, upon constant proof established$92397121 997 $aUNISA LEADER 01088nas 2200349 c 450 001 9910895339603321 005 20200903213907.0 011 $a2527-1253 035 $a(CKB)4100000011413658 035 $a(DE-101)1216716021 035 $a(DE-599)ZDB3035666-0 035 $a(EXLCZ)994100000011413658 100 $a20200828a20179999 |y | 101 0 $apor 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aControle social e desenvolvimento territorial$epesquisa e extensa?o : CSDT 210 31$aRio de Janeiro$cFundac?a?o Getulio Vargas$aSerope?dica$cUniversidade Federal Rural do Rio de Janeiro$aPalmas$cUniversidade Federal do Tocantins$d[2017]- 215 $aOnline-Ressource 300 $aGesehen am 28. August 2020 517 3 $aCSDT 608 $aZeitschrift$2gnd-content 676 $a300 801 0$b0355 801 1$bDE-101 801 2$b9001 906 $aJOURNAL 912 $a9910895339603321 996 $aControle social e desenvolvimento territorial$94246804 997 $aUNINA LEADER 03818nam 22006135 450 001 9910627248403321 005 20251113191552.0 010 $a981-19-5073-3 024 7 $a10.1007/978-981-19-5073-5 035 $a(MiAaPQ)EBC7102401 035 $a(Au-PeEL)EBL7102401 035 $a(CKB)24950544900041 035 $a(PPN)26495369X 035 $a(OCoLC)1348480841 035 $a(DE-He213)978-981-19-5073-5 035 $a(EXLCZ)9924950544900041 100 $a20220929d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aImproving Classifier Generalization $eReal-Time Machine Learning based Applications /$fby Rahul Kumar Sevakula, Nishchal K. Verma 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 online resource (181 pages) 225 1 $aStudies in Computational Intelligence,$x1860-9503 ;$v989 311 08$aPrint version: Sevakula, Rahul Kumar Improving Classifier Generalization Singapore : Springer,c2022 9789811950728 320 $aIncludes bibliographical references and index. 327 $aIntroduction to classification algorithms -- Methods to improve generalization performance -- MVPC ? a classifier with very low VC dimension -- Framework for reliable fault detection with sensor data -- Membership functions for Fuzzy Support Vector Machine in noisy environment -- Stacked Denoising Sparse Autoencoder based Fuzzy rule classifiers -- Epilogue. 330 $aThis book elaborately discusses techniques commonly used to improve generalization performance in classification approaches. The contents highlight methods to improve classification performance in numerous case studies: ranging from datasets of UCI repository to predictive maintenance problems and cancer classification problems. The book specifically provides a detailed tutorial on how to approach time-series classification problems and discusses two real time case studies on condition monitoring. In addition to describing the various aspects a data scientist must consider before finalizing their approach to a classification problem and reviewing the state of the art for improving classification generalization performance, it also discusses in detail the authors own contributions to the field, including MVPC - a classifier with very low VC dimension, a graphical indices based framework for reliable predictive maintenance and a novel general-purpose membership functions for Fuzzy Support Vector Machine which provides state of the art performance with noisy datasets, and a novel scheme to introduce deep learning in Fuzzy Rule based classifiers (FRCs). This volume will serve as a useful reference for researchers and students working on machine learning, health monitoring, predictive maintenance, time-series analysis, gene-expression data classification. . 410 0$aStudies in Computational Intelligence,$x1860-9503 ;$v989 606 $aMachine learning 606 $aComputational intelligence 606 $aPattern recognition systems 606 $aMachine Learning 606 $aComputational Intelligence 606 $aAutomated Pattern Recognition 615 0$aMachine learning. 615 0$aComputational intelligence. 615 0$aPattern recognition systems. 615 14$aMachine Learning. 615 24$aComputational Intelligence. 615 24$aAutomated Pattern Recognition. 676 $a629.8 700 $aSevakula$b Rahul Kumar$01267069 702 $aVerma$b Nishchal K. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910627248403321 996 $aImproving classifier generalization$93034247 997 $aUNINA