Computational Optimizations for Machine Learning

The present book contains the 10 articles finally accepted for publication in the Special Issue "Computational Optimizations for Machine Learning" of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networ...

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Other Authors: Gabbay, Freddy (Editor)
Format: Book Chapter
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
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DOAB: description of the publication
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245 1 0 |a Computational Optimizations for Machine Learning 
260 |a Basel  |b MDPI - Multidisciplinary Digital Publishing Institute  |c 2022 
300 |a 1 electronic resource (276 p.) 
506 0 |a Open Access  |2 star  |f Unrestricted online access 
520 |a The present book contains the 10 articles finally accepted for publication in the Special Issue "Computational Optimizations for Machine Learning" of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence. These topics include, among others, various types of machine learning classes, such as supervised, unsupervised and reinforcement learning, deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks and more. It is hoped that the book will be interesting and useful to those developing mathematical algorithms and applications in the domain of artificial intelligence and machine learning as well as for those having the appropriate mathematical background and willing to become familiar with recent advances of machine learning computational optimization mathematics, which has nowadays permeated into almost all sectors of human life and activity. 
540 |a Creative Commons  |f https://creativecommons.org/licenses/by/4.0/  |2 cc  |4 https://creativecommons.org/licenses/by/4.0/ 
546 |a English 
650 7 |a Research & information: general  |2 bicssc 
650 7 |a Mathematics & science  |2 bicssc 
653 |a ARIMA model 
653 |a time series analysis 
653 |a online optimization 
653 |a online model selection 
653 |a precipitation nowcasting 
653 |a deep learning 
653 |a autoencoders 
653 |a radar data 
653 |a generalization error 
653 |a recurrent neural networks 
653 |a machine learning 
653 |a model predictive control 
653 |a nonlinear systems 
653 |a neural networks 
653 |a low power 
653 |a quantization 
653 |a CNN architecture 
653 |a multi-objective optimization 
653 |a genetic algorithms 
653 |a evolutionary computation 
653 |a swarm intelligence 
653 |a Heating, Ventilation and Air Conditioning (HVAC) 
653 |a metaheuristics search 
653 |a bio-inspired algorithms 
653 |a smart building 
653 |a soft computing 
653 |a training 
653 |a evolution of weights 
653 |a artificial intelligence 
653 |a deep neural networks 
653 |a convolutional neural network 
653 |a deep compression 
653 |a DNN 
653 |a ReLU 
653 |a floating-point numbers 
653 |a hardware acceleration 
653 |a energy dissipation 
653 |a FLOW-3D 
653 |a hydraulic jumps 
653 |a bed roughness 
653 |a sensitivity analysis 
653 |a feature selection 
653 |a evolutionary algorithms 
653 |a nature inspired algorithms 
653 |a meta-heuristic optimization 
653 |a computational intelligence 
856 4 0 |a www.oapen.org  |u https://mdpi.com/books/pdfview/book/5018  |7 0  |z Get Fullteks 
856 4 0 |a www.oapen.org  |u https://directory.doabooks.org/handle/20.500.12854/79633  |7 0  |z DOAB: description of the publication