Improving Classifier Generalization Real-Time Machine Learning based Applications /
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 c...
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| Hlavní autoři: | , |
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| Korporativní autor: | |
| Médium: | Elektronický zdroj E-kniha |
| Jazyk: | angličtina |
| Vydáno: |
Singapore :
Springer Nature Singapore : Imprint: Springer,
2023.
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| Vydání: | 1st ed. 2023. |
| Edice: | Studies in Computational Intelligence,
989 |
| Témata: | |
| On-line přístup: | https://doi.org/10.1007/978-981-19-5073-5 |
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| Shrnutí: | 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. . |
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| Fyzický popis: | 1 online resource (XXIII, 166 p. 53 illus.). 45 illus. in color. |
| ISBN: | 9789811950735 |
| ISSN: | 1860-9503 ; |
| DOI: | 10.1007/978-981-19-5073-5 |