| 书目名称 | Improving Classifier Generalization |
| 副标题 | Real-Time Machine Le |
| 编辑 | Rahul Kumar Sevakula,Nishchal K. Verma |
| 视频video | |
| 概述 | Includes a special chapter on methods to improve generalization performance during classification.Case studies provide a "how to" for improving classification performance on numerous types of problems |
| 丛书名称 | Studies in Computational Intelligence |
| 图书封面 |  |
| 描述 | .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 use |
| 出版日期 | Book 2023 |
| 关键词 | Classification algorithms; Generalization performance; Predictive maintenance; Cancer classification; Tr |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-981-19-5073-5 |
| isbn_softcover | 978-981-19-5075-9 |
| isbn_ebook | 978-981-19-5073-5Series ISSN 1860-949X Series E-ISSN 1860-9503 |
| issn_series | 1860-949X |
| copyright | Springer Nature Singapore Pte Ltd. 2023 |
发表于 2025-3-21 16:55:19
