Название: Improving Classifier Generalization: Real-Time Machine Learning based Applications
Автор: Rahul Kumar Sevakula, Nishchal K. Verma
Издательство: Springer
Серия: Studies in Computational Intelligence
Год: 2023
Страниц: 181
Язык: английский
Формат: pdf
Размер: 10.1 MB
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. 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 (DL) in Fuzzy Rule based classifiers (FRCs). This monograph begins with the fundamentals of classifiers, bias-variance tradeoff, statistical learning theory (SLT), probably approximate correct (PAC) framework, maximum margin classifiers, and popular methods which improve generalization like regularization, boosting, transfer learning, dropout in Deep Learning, etc. Furthermore, the monograph solves four independent problems that have great relevance for certain real-time applications.
Название: Одной дождливой ночью
Название: Вампиры
Название: Enterprise Systems Architecture: Aligning Business Operating Models to Technology Landscapes
