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

Knowledge transfer between computer ...

Ionescu, Radu Tudor.

FindBook

Google Book

Amazon

博客來

Knowledge transfer between computer vision and text mining = similarity-based learning approaches /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Knowledge transfer between computer vision and text mining/ by Radu Tudor Ionescu, Marius Popescu.
其他題名:	similarity-based learning approaches /
作者:	Ionescu, Radu Tudor.
其他作者:	Popescu, Marius.
出版者:	Cham :Springer International Publishing : : 2016.,
面頁冊數:	xxiv, 250 p. :ill. (some col.), digital ;24 cm.
內容註:	Motivation and Overview -- Learning Based on Similarity -- Part I: Knowledge Transfer from Text Mining to Computer Vision -- State of the Art Approaches for Image Classification -- Local Displacement Estimation of Image Patches and Textons -- Object Recognition with the Bag of Visual Words Model -- Part II: Knowledge Transfer from Computer Vision to Text Mining -- State of the Art Approaches for String and Text Analysis -- Local Rank Distance -- Native Language Identification with String Kernels -- Spatial Information in Text Categorization -- Conclusions.
Contained By:	Springer eBooks
標題:	Machine learning. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-30367-3
ISBN:	9783319303673

Knowledge transfer between computer vision and text mining = similarity-based learning approaches /
Ionescu, Radu Tudor.

Knowledge transfer between computer vision and text miningsimilarity-based learning approaches /[electronic resource] :by Radu Tudor Ionescu, Marius Popescu. - Cham :Springer International Publishing :2016. - xxiv, 250 p. :ill. (some col.), digital ;24 cm. - Advances in computer vision and pattern recognition,2191-6586. - Advances in computer vision and pattern recognition..

Motivation and Overview -- Learning Based on Similarity -- Part I: Knowledge Transfer from Text Mining to Computer Vision -- State of the Art Approaches for Image Classification -- Local Displacement Estimation of Image Patches and Textons -- Object Recognition with the Bag of Visual Words Model -- Part II: Knowledge Transfer from Computer Vision to Text Mining -- State of the Art Approaches for String and Text Analysis -- Local Rank Distance -- Native Language Identification with String Kernels -- Spatial Information in Text Categorization -- Conclusions.

This ground-breaking text/reference diverges from the traditional view that computer vision (for image analysis) and string processing (for text mining) are separate and unrelated fields of study, propounding that images and text can be treated in a similar manner for the purposes of information retrieval, extraction and classification. Highlighting the benefits of knowledge transfer between the two disciplines, the text presents a range of novel similarity-based learning techniques founded on this approach. Topics and features: Describes a variety of similarity-based learning approaches, including nearest neighbor models, local learning, kernel methods, and clustering algorithms Presents a nearest neighbor model based on a novel dissimilarity for images, and applies this for handwritten digit recognition and texture analysis Discusses a novel kernel for (visual) word histograms, as well as several kernels based on pyramid representation, and uses these for facial expression recognition and text categorization by topic Introduces an approach based on string kernels for native language identification Contains links for downloading relevant open source code With a foreword by Prof. Florentina Hristea This unique work will be of great benefit to researchers, postgraduate and advanced undergraduate students involved in machine learning, data science, text mining and computer vision. Dr. Radu Tudor Ionescu is an Assistant Professor in the Department of Computer Science at the University of Bucharest, Romania. Dr. Marius Popescu is an Associate Professor at the same institution.

ISBN: 9783319303673

Standard No.: 10.1007/978-3-319-30367-3doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.3

Knowledge transfer between computer vision and text mining = similarity-based learning approaches /
LDR:03269nmm a2200337 a 4500 001 2035679
003 DE-He213
005 20161004092112.0
006 m d
007 cr nn 008maaau
008 161117s2016 gw s 0 eng d
020 $a 9783319303673 $q (electronic bk.)
020 $a 9783319303659 $q (paper)
024 7 $a 10.1007/978-3-319-30367-3 $2 doi
035 $a 978-3-319-30367-3
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UYQ $2 bicssc
072 7 $a TJFM1 $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 006.3 $2 23
090 $a Q325.5 $b .I64 2016
100 1 $a Ionescu, Radu Tudor. $3 2191231
245 1 0 $a Knowledge transfer between computer vision and text mining $h [electronic resource] : $b similarity-based learning approaches / $c by Radu Tudor Ionescu, Marius Popescu.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xxiv, 250 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Advances in computer vision and pattern recognition, $x 2191-6586
505 0 $a Motivation and Overview -- Learning Based on Similarity -- Part I: Knowledge Transfer from Text Mining to Computer Vision -- State of the Art Approaches for Image Classification -- Local Displacement Estimation of Image Patches and Textons -- Object Recognition with the Bag of Visual Words Model -- Part II: Knowledge Transfer from Computer Vision to Text Mining -- State of the Art Approaches for String and Text Analysis -- Local Rank Distance -- Native Language Identification with String Kernels -- Spatial Information in Text Categorization -- Conclusions.
520 $a This ground-breaking text/reference diverges from the traditional view that computer vision (for image analysis) and string processing (for text mining) are separate and unrelated fields of study, propounding that images and text can be treated in a similar manner for the purposes of information retrieval, extraction and classification. Highlighting the benefits of knowledge transfer between the two disciplines, the text presents a range of novel similarity-based learning techniques founded on this approach. Topics and features: Describes a variety of similarity-based learning approaches, including nearest neighbor models, local learning, kernel methods, and clustering algorithms Presents a nearest neighbor model based on a novel dissimilarity for images, and applies this for handwritten digit recognition and texture analysis Discusses a novel kernel for (visual) word histograms, as well as several kernels based on pyramid representation, and uses these for facial expression recognition and text categorization by topic Introduces an approach based on string kernels for native language identification Contains links for downloading relevant open source code With a foreword by Prof. Florentina Hristea This unique work will be of great benefit to researchers, postgraduate and advanced undergraduate students involved in machine learning, data science, text mining and computer vision. Dr. Radu Tudor Ionescu is an Assistant Professor in the Department of Computer Science at the University of Bucharest, Romania. Dr. Marius Popescu is an Associate Professor at the same institution.
650 0 $a Machine learning. $3 533906
650 0 $a Computer vision. $3 540671
650 0 $a Data mining. $3 562972
650 1 4 $a Computer Science. $3 626642
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
650 2 4 $a Image Processing and Computer Vision. $3 891070
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
700 1 $a Popescu, Marius. $3 2191232
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Advances in computer vision and pattern recognition. $3 1567575
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-30367-3
950 $a Computer Science (Springer-11645)