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