03818nam 22006135 450 991062724840332120251113191552.0981-19-5073-310.1007/978-981-19-5073-5(MiAaPQ)EBC7102401(Au-PeEL)EBL7102401(CKB)24950544900041(PPN)26495369X(OCoLC)1348480841(DE-He213)978-981-19-5073-5(EXLCZ)992495054490004120220929d2023 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierImproving Classifier Generalization Real-Time Machine Learning based Applications /by Rahul Kumar Sevakula, Nishchal K. Verma1st ed. 2023.Singapore :Springer Nature Singapore :Imprint: Springer,2023.1 online resource (181 pages)Studies in Computational Intelligence,1860-9503 ;989Print version: Sevakula, Rahul Kumar Improving Classifier Generalization Singapore : Springer,c2022 9789811950728 Includes bibliographical references and index.Introduction 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.This 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. .Studies in Computational Intelligence,1860-9503 ;989Machine learningComputational intelligencePattern recognition systemsMachine LearningComputational IntelligenceAutomated Pattern RecognitionMachine learning.Computational intelligence.Pattern recognition systems.Machine Learning.Computational Intelligence.Automated Pattern Recognition.629.8Sevakula Rahul Kumar1267069Verma Nishchal K.MiAaPQMiAaPQMiAaPQBOOK9910627248403321Improving classifier generalization3034247UNINA