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Implementations and applications of machine learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	Implementations and applications of machine learningedited by Saad Subair, Christopher Thron.
other author:	Subair, Saad.
Published:	Cham :Springer International Publishing :2020.
Description:	xii, 280 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-3-030-37830-1
ISBN:	9783030378301$q(electronic bk.)

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. 216..

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$q(electronic bk.)

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

188639
Machine learning.

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

Dewey Class. No.: 006.31

Implementations and applications of machine learning
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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.
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