Improving Classifier Generalization: Real-Time Machine Learning based Applications: 989 (Studies in Computational Intelligence) - Tapa blanda

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Dr Sevakula Rahul Kumar has over 10 years of research experience in machine learning (ML) and deep learning (DL). He received his Bachelor’s degree from the National Institute of Technology (NIT) Warangal, India in 2009 and later his Ph.D. degree from the Indian Institute of Technology (IIT) Kanpur, India in 2017. He is currently a Sr. Research Scientist at Whoop, and his research interests lie at the intersection of ML, physiological signals, cardiovascular health monitoring (medicine) and wearables. Prior to joining Whoop, he was an Instructor (junior research faculty) at Harvard Medical School and Massachusetts General Hospital, USA, and a Data Scientist at IBM India. He has filed multiple patent disclosures and has published over 45 research papers in international peer-reviewed journals and conferences. He is also a reviewer for several journals of national and international repute.

Dr. Nishchal K. Verma is a Professor in the Department of Electrical Engineering at Indian Institute of Technology (IIT) Kanpur, India.  Dr. Verma's research interest falls in Artificial Intelligence (AI) related theories and its practical applications to inter-disciplinary domains like machine learning, deep learning, computer vision, prognosis and health management, bioinformatics, cyber-physical systems, complex and highly non-linear systems modeling, clustering, and classifications, etc. He has published more than 250 research papers in peer-reviewed reputed conferences and journals along with 4 books (edited/ co-authored) in the field of AI. He has 20+ years of experience in the field of AI. He is currently serving as Associate Editor/ Editorial Board Member of various reputed journals and conferences. He has also developed several AI-related key technologies for The BOEING Company, USA.

De la contraportada

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.