Implementations and Applications of Machine Learning [electronic resource] /
edited by Saad Subair, Christopher Thron.
- 1st ed. 2020.
- XII, 280 p. 120 illus., 92 illus. in color. online resource.
- Studies in Computational Intelligence, 782 1860-9503 ; .
- Studies in Computational Intelligence, 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.
9783030378301
10.1007/978-3-030-37830-1 doi
Telecommunication.
Computational intelligence.
Data mining.
Dynamics.
Nonlinear theories.
Medical informatics.
Bioinformatics.
Communications Engineering, Networks.
Computational Intelligence.
Data Mining and Knowledge Discovery.
Applied Dynamical Systems.
Health Informatics.
Bioinformatics.
TK5101-5105.9
621.382
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.
9783030378301
10.1007/978-3-030-37830-1 doi
Telecommunication.
Computational intelligence.
Data mining.
Dynamics.
Nonlinear theories.
Medical informatics.
Bioinformatics.
Communications Engineering, Networks.
Computational Intelligence.
Data Mining and Knowledge Discovery.
Applied Dynamical Systems.
Health Informatics.
Bioinformatics.
TK5101-5105.9
621.382