國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Machine learning paradigmsadvances i...

Jain, Lakhmi C.

Machine learning paradigmsadvances in deep learning-based technological applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning paradigmsedited by George A. Tsihrintzis, Lakhmi C. Jain.
其他題名:	advances in deep learning-based technological applications /
其他作者:	Tsihrintzis, George A.
出版者:	Cham :Springer International Publishing :2020.
面頁冊數:	xii, 430 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Machine learning.
電子資源:	https://doi.org/10.1007/978-3-030-49724-8
ISBN:	9783030497248$q(electronic bk.)

Machine learning paradigmsadvances in deep learning-based technological applications /
Machine learning paradigmsadvances in deep learning-based technological applications /[electronic resource] :edited by George A. Tsihrintzis, Lakhmi C. Jain. - Cham :Springer International Publishing :2020. - xii, 430 p. :ill., digital ;24 cm. - Learning and analytics in intelligent systems,v.182662-3447 ;. - Learning and analytics in intelligent systems ;v.1..

Chapter 1: Introduction to Deep Learning-based Technological Applications -- Chapter 2: Vision to Language: Methods, Metrics and Datasets -- Chapter 3: Deep Learning Techniques for Geospatial Data Analysis -- Chapter 4: Deep Learning Approaches in Food Recognition -- Chapter 5: Deep Learning for Twitter Sentiment Analysis: the Effect of pre-trained Word Embedding -- Chapter 6: A Good Defense is a Strong DNN: Defending the IoT with Deep Neural Networks -- Chapter 7: Survey on Deep Learning Techniques for Medical Imaging Application Area -- Chapter 8: Deep Learning Methods in Electroencephalography.

At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in deep learning-based technological applications and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1) Deep Learning in Sensing, (2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical Field, (4) Deep Learning in Systems Control, (5) Deep Learning in Feature Vector Processing, and (6) Evaluation of Algorithm Performance. This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.

ISBN: 9783030497248$q(electronic bk.)

Standard No.: 10.1007/978-3-030-49724-8doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .M334 2020

Dewey Class. No.: 006.31

Machine learning paradigmsadvances in deep learning-based technological applications /
LDR:03183nmm a2200337 a 4500 001 583762
003 DE-He213
005 20201117165411.0
006 m d
007 cr nn 008maaau
008 210202s2020 sz s 0 eng d
020 $a 9783030497248$q(electronic bk.)
020 $a 9783030497231$q(paper)
024 7 $a 10.1007/978-3-030-49724-8 $2 doi
035 $a 978-3-030-49724-8
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5 $b .M334 2020
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 .M149 2020
245 0 0 $a Machine learning paradigms $h [electronic resource] : $b advances in deep learning-based technological applications / $c edited by George A. Tsihrintzis, Lakhmi C. Jain.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xii, 430 p. : $b ill., digital ; $c 24 cm.
490 1 $a Learning and analytics in intelligent systems, $x 2662-3447 ; $v v.18
505 0 $a Chapter 1: Introduction to Deep Learning-based Technological Applications -- Chapter 2: Vision to Language: Methods, Metrics and Datasets -- Chapter 3: Deep Learning Techniques for Geospatial Data Analysis -- Chapter 4: Deep Learning Approaches in Food Recognition -- Chapter 5: Deep Learning for Twitter Sentiment Analysis: the Effect of pre-trained Word Embedding -- Chapter 6: A Good Defense is a Strong DNN: Defending the IoT with Deep Neural Networks -- Chapter 7: Survey on Deep Learning Techniques for Medical Imaging Application Area -- Chapter 8: Deep Learning Methods in Electroencephalography.
520 $a At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in deep learning-based technological applications and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1) Deep Learning in Sensing, (2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical Field, (4) Deep Learning in Systems Control, (5) Deep Learning in Feature Vector Processing, and (6) Evaluation of Algorithm Performance. This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.
650 0 $a Machine learning. $3 188639
650 1 4 $a Machine Learning. $3 833608
650 2 4 $a Computational Intelligence. $3 338479
700 1 $a Tsihrintzis, George A. $3 284081
700 1 $a Jain, Lakhmi C. $3 276563
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Learning and analytics in intelligent systems ; $v v.1. $3 848241
856 4 0 $u https://doi.org/10.1007/978-3-030-49724-8
950 $a Mathematics and Statistics (SpringerNature-11649)