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

Learning representation for multi-vi...

Ding, Zhengming.

FindBook

Google Book

Amazon

博客來

Learning representation for multi-view data analysis = models and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Learning representation for multi-view data analysis/ by Zhengming Ding, Handong Zhao, Yun Fu.
其他題名:	models and applications /
作者:	Ding, Zhengming.
其他作者:	Zhao, Handong.
出版者:	Cham :Springer International Publishing : : 2019.,
面頁冊數:	x, 268 p. :ill., digital ;24 cm.
內容註:	Introduction -- Multi-view Clustering with Complete Information -- Multi-view Clustering with Partial Information -- Multi-view Outlier Detection -- Multi-view Transformation Learning -- Zero-Shot Learning -- Missing Modality Transfer Learning -- Deep Domain Adaptation -- Deep Domain Generalization.
Contained By:	Springer eBooks
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-3-030-00734-8
ISBN:	9783030007348

Learning representation for multi-view data analysis = models and applications /
Ding, Zhengming.

Learning representation for multi-view data analysismodels and applications /[electronic resource] :by Zhengming Ding, Handong Zhao, Yun Fu. - Cham :Springer International Publishing :2019. - x, 268 p. :ill., digital ;24 cm. - Advanced information and knowledge processing,1610-3947. - Advanced information and knowledge processing..

Introduction -- Multi-view Clustering with Complete Information -- Multi-view Clustering with Partial Information -- Multi-view Outlier Detection -- Multi-view Transformation Learning -- Zero-Shot Learning -- Missing Modality Transfer Learning -- Deep Domain Adaptation -- Deep Domain Generalization.

This book equips readers to handle complex multi-view data representation, centered around several major visual applications, sharing many tips and insights through a unified learning framework. This framework is able to model most existing multi-view learning and domain adaptation, enriching readers' understanding from their similarity, and differences based on data organization and problem settings, as well as the research goal. A comprehensive review exhaustively provides the key recent research on multi-view data analysis, i.e., multi-view clustering, multi-view classification, zero-shot learning, and domain adaption. More practical challenges in multi-view data analysis are discussed including incomplete, unbalanced and large-scale multi-view learning. Learning Representation for Multi-View Data Analysis covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.

ISBN: 9783030007348

Standard No.: 10.1007/978-3-030-00734-8doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Learning representation for multi-view data analysis = models and applications /
LDR:02386nmm a2200349 a 4500 001 2178338
003 DE-He213
005 20181206131733.0
006 m d
007 cr nn 008maaau
008 191122s2019 gw s 0 eng d
020 $a 9783030007348 $q (electronic bk.)
020 $a 9783030007331 $q (paper)
024 7 $a 10.1007/978-3-030-00734-8 $2 doi
035 $a 978-3-030-00734-8
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UNF $2 bicssc
072 7 $a COM021030 $2 bisacsh
072 7 $a UNF $2 thema
072 7 $a UYQE $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .D584 2019
100 1 $a Ding, Zhengming. $3 3382411
245 1 0 $a Learning representation for multi-view data analysis $h [electronic resource] : $b models and applications / $c by Zhengming Ding, Handong Zhao, Yun Fu.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a x, 268 p. : $b ill., digital ; $c 24 cm.
490 1 $a Advanced information and knowledge processing, $x 1610-3947
505 0 $a Introduction -- Multi-view Clustering with Complete Information -- Multi-view Clustering with Partial Information -- Multi-view Outlier Detection -- Multi-view Transformation Learning -- Zero-Shot Learning -- Missing Modality Transfer Learning -- Deep Domain Adaptation -- Deep Domain Generalization.
520 $a This book equips readers to handle complex multi-view data representation, centered around several major visual applications, sharing many tips and insights through a unified learning framework. This framework is able to model most existing multi-view learning and domain adaptation, enriching readers' understanding from their similarity, and differences based on data organization and problem settings, as well as the research goal. A comprehensive review exhaustively provides the key recent research on multi-view data analysis, i.e., multi-view clustering, multi-view classification, zero-shot learning, and domain adaption. More practical challenges in multi-view data analysis are discussed including incomplete, unbalanced and large-scale multi-view learning. Learning Representation for Multi-View Data Analysis covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.
650 0 $a Machine learning. $3 533906
650 0 $a Learning models (Stochastic processes) $3 793031
650 1 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Pattern Recognition. $3 891045
700 1 $a Zhao, Handong. $3 3382412
700 1 $a Fu, Yun. $3 2059227
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Advanced information and knowledge processing. $3 1568369
856 4 0 $u https://doi.org/10.1007/978-3-030-00734-8
950 $a Computer Science (Springer-11645)