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Book synopsis

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. 

Book details

Book category Books in English Computing & information technology Computer science Artificial intelligence

• Full title: Improving Classifier Generalization
• Subtitle: Real-Time Machine Learning based Applications
• Author: Rahul Kumar Sevakula, Nishchal K. Verma
• Language: English
• Binding: Hardback
• Number of pages: 166
• EAN: 9789811950728
• ID: 39364473
• Publisher: Springer, Berlin
• Weight: 494 g
• Dimensions: 235 × 155 × 16 mm
• Published: 2022

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