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Improving classifier generalization ...

Sevakula, Rahul Kumar.

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Improving classifier generalization = real-time machine learning based applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Improving classifier generalization/ by Rahul Kumar Sevakula, Nishchal K. Verma.
Reminder of title:	real-time machine learning based applications /
Author:	Sevakula, Rahul Kumar.
other author:	Verma, Nishchal K.
Published:	Singapore :Springer Nature Singapore : : 2023.,
Description:	xxiii, 166 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction to classification algorithms -- Methods to improve generalization performance -- MVPC - a classifier with very low VC dimension -- Framework for reliable fault detection with sensor data -- Membership functions for Fuzzy Support Vector Machine in noisy environment -- Stacked Denoising Sparse Autoencoder based Fuzzy rule classifiers -- Epilogue.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-981-19-5073-5
ISBN:	9789811950735

Improving classifier generalization = real-time machine learning based applications /
Sevakula, Rahul Kumar.

Improving classifier generalizationreal-time machine learning based applications /[electronic resource] :by Rahul Kumar Sevakula, Nishchal K. Verma. - Singapore :Springer Nature Singapore :2023. - xxiii, 166 p. :ill., digital ;24 cm. - Studies in computational intelligence,v. 9891860-9503 ;. - Studies in computational intelligence ;v. 989..

Introduction to classification algorithms -- Methods to improve generalization performance -- MVPC - a classifier with very low VC dimension -- Framework for reliable fault detection with sensor data -- Membership functions for Fuzzy Support Vector Machine in noisy environment -- Stacked Denoising Sparse Autoencoder based Fuzzy rule classifiers -- Epilogue.

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.

ISBN: 9789811950735

Standard No.: 10.1007/978-981-19-5073-5doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Improving classifier generalization = real-time machine learning based applications /
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520 $a 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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