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

回上頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Multi-objective, multi-class and multi-label data classification with class imbalance = theory and practices /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Multi-objective, multi-class and multi-label data classification with class imbalance/ by Sanjay Chakraborty, Lopamudra Dey.
其他題名:	theory and practices /
作者:	Chakraborty, Sanjay.
其他作者:	Dey, Lopamudra.
出版者:	Singapore :Springer Nature Singapore : : 2024.,
面頁冊數:	xviii, 164 p. :ill. (chiefly color), digital ;24 cm.
內容註:	1. Introduction to Classification -- 2. Class Imbalance and Data Irregularities in Classification -- 3. Multi-class Classification -- 4. Multi-Objective and Multi-Label Classification -- 5. Deep Learning Inspired Multiclass and Multilabel Classification -- 6. Applications of Multi-objective, Multi-label and Multi-class Classifications.
Contained By:	Springer Nature eBook
標題:	Classification rule mining. -
電子資源:	https://doi.org/10.1007/978-981-97-9622-9
ISBN:	9789819796229

Multi-objective, multi-class and multi-label data classification with class imbalance = theory and practices /
Chakraborty, Sanjay.

Multi-objective, multi-class and multi-label data classification with class imbalancetheory and practices /[electronic resource] :by Sanjay Chakraborty, Lopamudra Dey. - Singapore :Springer Nature Singapore :2024. - xviii, 164 p. :ill. (chiefly color), digital ;24 cm. - Springer tracts in nature-inspired computing,2524-5538. - Springer tracts in nature-inspired computing..

1. Introduction to Classification -- 2. Class Imbalance and Data Irregularities in Classification -- 3. Multi-class Classification -- 4. Multi-Objective and Multi-Label Classification -- 5. Deep Learning Inspired Multiclass and Multilabel Classification -- 6. Applications of Multi-objective, Multi-label and Multi-class Classifications.

This book explores intricate world of data classification with 'Multi-Objective, Multi-Class, and Multi-Label Data Classification.' This book studies sophisticated methods and strategies for working with complicated data sets, tackling the difficulties of various classes, many objectives, and complicated labelling tasks. This resource fosters a deeper grasp of multi-dimensional data analysis in today's data-driven world by providing readers with the skills and insights needed to navigate the subtleties of modern classification jobs, from algorithmic techniques to practical applications.

ISBN: 9789819796229

Standard No.: 10.1007/978-981-97-9622-9doiSubjects--Topical Terms:

2209888
Classification rule mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Multi-objective, multi-class and multi-label data classification with class imbalance = theory and practices /
LDR:02091nmm a2200337 a 4500 001 2389054
003 DE-He213
005 20241223115547.0
006 m d
007 cr nn 008maaau
008 250916s2024 si s 0 eng d
020 $a 9789819796229 $q (electronic bk.)
020 $a 9789819796212 $q (paper)
024 7 $a 10.1007/978-981-97-9622-9 $2 doi
035 $a 978-981-97-9622-9
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D343
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.312 $2 23
090 $a QA76.9.D343 $b C435 2024
100 1 $a Chakraborty, Sanjay. $3 3598763
245 1 0 $a Multi-objective, multi-class and multi-label data classification with class imbalance $h [electronic resource] : $b theory and practices / $c by Sanjay Chakraborty, Lopamudra Dey.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2024.
300 $a xviii, 164 p. : $b ill. (chiefly color), digital ; $c 24 cm.
490 1 $a Springer tracts in nature-inspired computing, $x 2524-5538
505 0 $a 1. Introduction to Classification -- 2. Class Imbalance and Data Irregularities in Classification -- 3. Multi-class Classification -- 4. Multi-Objective and Multi-Label Classification -- 5. Deep Learning Inspired Multiclass and Multilabel Classification -- 6. Applications of Multi-objective, Multi-label and Multi-class Classifications.
520 $a This book explores intricate world of data classification with 'Multi-Objective, Multi-Class, and Multi-Label Data Classification.' This book studies sophisticated methods and strategies for working with complicated data sets, tackling the difficulties of various classes, many objectives, and complicated labelling tasks. This resource fosters a deeper grasp of multi-dimensional data analysis in today's data-driven world by providing readers with the skills and insights needed to navigate the subtleties of modern classification jobs, from algorithmic techniques to practical applications.
650 0 $a Classification rule mining. $3 2209888
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Machine Learning. $3 3382522
700 1 $a Dey, Lopamudra. $3 3629066
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Springer tracts in nature-inspired computing. $3 3443947
856 4 0 $u https://doi.org/10.1007/978-981-97-9622-9
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)