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200506 ||| eng |
| 020 |
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|a 9783030378301
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| 100 |
1 |
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|a Subair, Saad
|e [editor]
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| 245 |
0 |
0 |
|a Implementations and Applications of Machine Learning
|h Elektronische Ressource
|c edited by Saad Subair, Christopher Thron
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| 250 |
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|a 1st ed. 2020
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2020, 2020
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| 300 |
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|a XII, 280 p. 120 illus., 92 illus. in color
|b online resource
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| 505 |
0 |
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|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
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| 653 |
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|a Health Informatics
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| 653 |
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|a Bioinformatics
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| 653 |
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|a Computational intelligence
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| 653 |
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|a Applied Dynamical Systems
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| 653 |
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|a Medical informatics
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| 653 |
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|a Data mining
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| 653 |
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|a Computational Intelligence
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| 653 |
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|a Nonlinear theories
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| 653 |
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|a Telecommunication
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| 653 |
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|a Communications Engineering, Networks
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| 653 |
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|a Data Mining and Knowledge Discovery
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| 653 |
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|a Dynamics
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| 700 |
1 |
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|a Thron, Christopher
|e [editor]
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
|
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|b Springer
|a Springer eBooks 2005-
|
| 490 |
0 |
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|a Studies in Computational Intelligence
|
| 028 |
5 |
0 |
|a 10.1007/978-3-030-37830-1
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-030-37830-1?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 621.382
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| 520 |
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|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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