Improving Classifier Generalization : Real-Time Machine Learning based Applications / / by Rahul Kumar Sevakula, Nishchal K. Verma

• Autore: Sevakula Rahul Kumar
• Pubblicazione: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023
• Edizione: 1st ed. 2023.
• Descrizione fisica: 1 online resource (181 pages)
• Disciplina: 629.8
• Soggetto topico: Machine learning; Computational intelligence; Pattern recognition systems; Machine Learning; Computational Intelligence; Automated Pattern Recognition
• Persona (resp. second.): VermaNishchal K.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 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.
• Sommario/riassunto: 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. .
• Titolo autorizzato: Improving classifier generalization
• ISBN: 981-19-5073-3
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910627248403321
• Lo trovi qui: Univ. Federico II
• Serie: Studies in Computational Intelligence, . 1860-9503 ; ; 989