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Learning approaches in signal proces...

Siu, Wan-Chi.

Learning approaches in signal processing[electronic resource] /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	006.31
書名/作者:	Learning approaches in signal processing/ edited by Wan-Chi Siu ... [et al.].
其他作者:	Siu, Wan-Chi.
出版者:	Singapore : : Pan Stanford Publishing,, c2018.
面頁冊數:	1 online resource (678 p.) : : ill.
標題:	Machine learning.
標題:	Signal processing.
ISBN:	9780429061141
書目註:	Includes bibliographical references and index.
摘要、提要註:	Coupled with machine learning, the use of signal processing techniques for big data analysis, Internet of things, smart cities, security, and bio-informatics applications has witnessed explosive growth. This has been made possible via fast algorithms on data, speech, image, and video processing with advanced GPU technology. This book presents an up-to-date tutorial and overview on learning technologies such as random forests, sparsity, and low-rank matrix estimation andcutting-edge visual/signal processing techniques, including face recognition, Kalman filtering, and multirate DSP. It discusses the applications that make use of deep learning, convolutional neural networks, random forests, etc. The applications include super-resolution imaging, fringe projection profilometry, human activities detection/capture, gesture recognition, spoken language processing, cooperative networks, bioinformatics, DNA, and healthcare.
電子資源:	https://www.taylorfrancis.com/books/9780429061141

Learning approaches in signal processing[electronic resource] /
Learning approaches in signal processing[electronic resource] /edited by Wan-Chi Siu ... [et al.]. - 1st ed. - Singapore :Pan Stanford Publishing,c2018. - 1 online resource (678 p.) :ill. - Pan Stanford series on digital signal processing ;v. 2. - Pan Stanford series on digital signal processing ;v. 2..

Includes bibliographical references and index.

Coupled with machine learning, the use of signal processing techniques for big data analysis, Internet of things, smart cities, security, and bio-informatics applications has witnessed explosive growth. This has been made possible via fast algorithms on data, speech, image, and video processing with advanced GPU technology. This book presents an up-to-date tutorial and overview on learning technologies such as random forests, sparsity, and low-rank matrix estimation andcutting-edge visual/signal processing techniques, including face recognition, Kalman filtering, and multirate DSP. It discusses the applications that make use of deep learning, convolutional neural networks, random forests, etc. The applications include super-resolution imaging, fringe projection profilometry, human activities detection/capture, gesture recognition, spoken language processing, cooperative networks, bioinformatics, DNA, and healthcare.

ISBN: 9780429061141Subjects--Topical Terms:

202931
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Learning approaches in signal processing[electronic resource] /
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020 $a 9780429061141 $q (electronic bk. ; $q PDF)
020 $z 9789814800518 $q (Paperback)
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040 $a FlBoTFG $b eng $c FlBoTFG
041 0 $a eng
050 4 $a Q325.5
082 0 4 $a 006.31 $2 23
245 0 0 $a Learning approaches in signal processing $h [electronic resource] / $c edited by Wan-Chi Siu ... [et al.].
250 $a 1st ed.
260 $a Singapore : $b Pan Stanford Publishing, $c c2018.
300 $a 1 online resource (678 p.) : $b ill.
490 1 $a Pan Stanford series on digital signal processing ; $v v. 2
504 $a Includes bibliographical references and index.
520 3 $a Coupled with machine learning, the use of signal processing techniques for big data analysis, Internet of things, smart cities, security, and bio-informatics applications has witnessed explosive growth. This has been made possible via fast algorithms on data, speech, image, and video processing with advanced GPU technology. This book presents an up-to-date tutorial and overview on learning technologies such as random forests, sparsity, and low-rank matrix estimation andcutting-edge visual/signal processing techniques, including face recognition, Kalman filtering, and multirate DSP. It discusses the applications that make use of deep learning, convolutional neural networks, random forests, etc. The applications include super-resolution imaging, fringe projection profilometry, human activities detection/capture, gesture recognition, spoken language processing, cooperative networks, bioinformatics, DNA, and healthcare.
588 $a Description based on print version record.
650 0 $a Machine learning. $3 202931
650 0 $a Signal processing. $3 174043
700 1 $a Siu, Wan-Chi. $3 710359
830 0 $a Pan Stanford series on digital signal processing ; $v v. 2. $3 710360
856 4 0 $u https://www.taylorfrancis.com/books/9780429061141

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