東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Improving classifier generalization ...

Sevakula, Rahul Kumar.

FindBook

Google Book

Amazon

博客來

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Improving classifier generalization/ by Rahul Kumar Sevakula, Nishchal K. Verma.
其他題名:	real-time machine learning based applications /
作者:	Sevakula, Rahul Kumar.
其他作者:	Verma, Nishchal K.
出版者:	Singapore :Springer Nature Singapore : : 2023.,
面頁冊數:	xxiii, 166 p. :ill., digital ;24 cm.
內容註:	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
標題:	Machine learning. -
電子資源:	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 /
LDR:02825nmm a2200337 a 4500 001 2314438
003 DE-He213
005 20220929180648.0
006 m d
007 cr nn 008maaau
008 230902s2023 si s 0 eng d
020 $a 9789811950735 $q (electronic bk.)
020 $a 9789811950728 $q (paper)
024 7 $a 10.1007/978-981-19-5073-5 $2 doi
035 $a 978-981-19-5073-5
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UYQM $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .S497 2023
100 1 $a Sevakula, Rahul Kumar. $3 3625829
245 1 0 $a Improving classifier generalization $h [electronic resource] : $b real-time machine learning based applications / $c by Rahul Kumar Sevakula, Nishchal K. Verma.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2023.
300 $a xxiii, 166 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-9503 ; $v v. 989
505 0 $a 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.
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.
650 0 $a Machine learning. $3 533906
650 0 $a Automatic classification. $3 1569653
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Automated Pattern Recognition. $3 3538549
700 1 $a Verma, Nishchal K. $3 3378823
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Studies in computational intelligence ; $v v. 989. $3 3625830
856 4 0 $u https://doi.org/10.1007/978-981-19-5073-5
950 $a Engineering (SpringerNature-11647)