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

Implementations and applications of ...

Subair, Saad.

FindBook

Google Book

Amazon

博客來

Implementations and applications of machine learning

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Implementations and applications of machine learning/ edited by Saad Subair, Christopher Thron.
其他作者:	Subair, Saad.
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	xii, 280 p. :ill., digital ;24 cm.
內容註:	Introduction -- Part 1: Machine learning concepts, methods, and software tools -- Overview -- Classifying algorithms -- Support vector machines -- Bayes classifiers -- Decision trees -- Clustering algorithms -- k-means and variants -- Gaussian mixture -- Association rules -- Optimization algorithms -- Genetic algorithms -- Swarm intelligence -- Deep learning,- Convolutional neural networks (CNN) -- Other deep learning schema -- Part 2: Applications with implementations -- Protein secondary structure prediction -- Mapping heart disease risk -- Surgical performance monitoring -- Power grid control -- Conclusion.
Contained By:	Springer eBooks
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-3-030-37830-1
ISBN:	9783030378301

Implementations and applications of machine learning
Implementations and applications of machine learning[electronic resource] /edited by Saad Subair, Christopher Thron. - Cham :Springer International Publishing :2020. - xii, 280 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.7821860-949X ;. - Studies in computational intelligence ;v.782..

Introduction -- Part 1: Machine learning concepts, methods, and software tools -- Overview -- Classifying algorithms -- Support vector machines -- Bayes classifiers -- Decision trees -- Clustering algorithms -- k-means and variants -- Gaussian mixture -- Association rules -- Optimization algorithms -- Genetic algorithms -- Swarm intelligence -- Deep learning,- Convolutional neural networks (CNN) -- Other deep learning schema -- Part 2: Applications with implementations -- Protein secondary structure prediction -- Mapping heart disease risk -- Surgical performance monitoring -- Power grid control -- Conclusion.

This book provides step-by-step explanations of successful implementations and practical applications of machine learning. The book's GitHub page contains software codes to assist readers in adapting materials and methods for their own use. A wide variety of applications are discussed, including wireless mesh network and power systems optimization; computer vision; image and facial recognition; protein prediction; data mining; and data discovery. Numerous state-of-the-art machine learning techniques are employed (with detailed explanations), including biologically-inspired optimization (genetic and other evolutionary algorithms, swarm intelligence); Viola Jones face detection; Gaussian mixture modeling; support vector machines; deep convolutional neural networks with performance enhancement techniques (including network design, learning rate optimization, data augmentation, transfer learning); spiking neural networks and timing dependent plasticity; frequent itemset mining; binary classification; and dynamic programming. This book provides valuable information on effective, cutting-edge techniques, and approaches for students, researchers, practitioners, and teachers in the field of machine learning. Presents practical, useful applications of machine learning for practitioners, students, and researchers Provides hands-on tools for a variety of machine learning techniques Covers evolutionary and swarm intelligence, facial and image recognition, deep learning, data mining and discovery, and statistical techniques.

ISBN: 9783030378301

Standard No.: 10.1007/978-3-030-37830-1doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .I475 2020

Dewey Class. No.: 006.31

Implementations and applications of machine learning
LDR:03204nmm a2200337 a 4500 001 2217770
003 DE-He213
005 20200814142228.0
006 m d
007 cr nn 008maaau
008 201120s2020 sz s 0 eng d
020 $a 9783030378301 $q (electronic bk.)
020 $a 9783030378295 $q (paper)
024 7 $a 10.1007/978-3-030-37830-1 $2 doi
035 $a 978-3-030-37830-1
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5 $b .I475 2020
072 7 $a TJK $2 bicssc
072 7 $a TEC041000 $2 bisacsh
072 7 $a TJK $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .I34 2020
245 0 0 $a Implementations and applications of machine learning $h [electronic resource] / $c edited by Saad Subair, Christopher Thron.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xii, 280 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-949X ; $v v.782
505 0 $a Introduction -- Part 1: Machine learning concepts, methods, and software tools -- Overview -- Classifying algorithms -- Support vector machines -- Bayes classifiers -- Decision trees -- Clustering algorithms -- k-means and variants -- Gaussian mixture -- Association rules -- Optimization algorithms -- Genetic algorithms -- Swarm intelligence -- Deep learning,- Convolutional neural networks (CNN) -- Other deep learning schema -- Part 2: Applications with implementations -- Protein secondary structure prediction -- Mapping heart disease risk -- Surgical performance monitoring -- Power grid control -- Conclusion.
520 $a This book provides step-by-step explanations of successful implementations and practical applications of machine learning. The book's GitHub page contains software codes to assist readers in adapting materials and methods for their own use. A wide variety of applications are discussed, including wireless mesh network and power systems optimization; computer vision; image and facial recognition; protein prediction; data mining; and data discovery. Numerous state-of-the-art machine learning techniques are employed (with detailed explanations), including biologically-inspired optimization (genetic and other evolutionary algorithms, swarm intelligence); Viola Jones face detection; Gaussian mixture modeling; support vector machines; deep convolutional neural networks with performance enhancement techniques (including network design, learning rate optimization, data augmentation, transfer learning); spiking neural networks and timing dependent plasticity; frequent itemset mining; binary classification; and dynamic programming. This book provides valuable information on effective, cutting-edge techniques, and approaches for students, researchers, practitioners, and teachers in the field of machine learning. Presents practical, useful applications of machine learning for practitioners, students, and researchers Provides hands-on tools for a variety of machine learning techniques Covers evolutionary and swarm intelligence, facial and image recognition, deep learning, data mining and discovery, and statistical techniques.
650 0 $a Machine learning. $3 533906
650 1 4 $a Communications Engineering, Networks. $3 891094
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Complexity. $3 893807
650 2 4 $a Health Informatics. $3 892928
650 2 4 $a Bioinformatics. $3 553671
700 1 $a Subair, Saad. $3 3451250
700 1 $a Thron, Christopher. $3 3451251
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Studies in computational intelligence ; $v v.782. $3 3451252
856 4 0 $u https://doi.org/10.1007/978-3-030-37830-1
950 $a Engineering (Springer-11647)