Improving Classifier Generalization : Real-Time Machine Learning based Applications Paperback / softback
by Rahul Kumar Sevakula, Nishchal K. Verma
Part of the Studies in Computational Intelligence series
Paperback / softback
Description
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.
Information
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Out of stock
- Format:Paperback / softback
- Pages:166 pages, 45 Illustrations, color; 8 Illustrations, black and white; XXIII, 166 p. 53 illus., 45 il
- Publisher:Springer Verlag, Singapore
- Publication Date:02/10/2023
- Category:
- ISBN:9789811950759
Other Formats
- Hardback from £123.05
Information
-
Out of stock
- Format:Paperback / softback
- Pages:166 pages, 45 Illustrations, color; 8 Illustrations, black and white; XXIII, 166 p. 53 illus., 45 il
- Publisher:Springer Verlag, Singapore
- Publication Date:02/10/2023
- Category:
- ISBN:9789811950759
