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 networks and art...

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Médium:	E-kniha
Jazyk:	English
Vydavateľské údaje:	Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
Predmet:	ARIMA model artificial intelligence autoencoders bed roughness bio-inspired algorithms CNN architecture computational intelligence convolutional neural network deep compression deep learning deep neural networks DNN energy dissipation evolution of weights evolutionary algorithms evolutionary computation feature selection floating-point numbers FLOW-3D generalization error genetic algorithms hardware acceleration Heating hydraulic jumps low power machine learning Mathematics and Science meta-heuristic optimization metaheuristics search model predictive control multi-objective optimization nature inspired algorithms neural networks nonlinear systems online model selection online optimization precipitation nowcasting quantization radar data recurrent neural networks Reference, Information and Interdisciplinary subjects ReLU Research and information: general sensitivity analysis smart building soft computing swarm intelligence time series analysis training Ventilation and Air Conditioning (HVAC)
ISBN:	3036531874, 3036531866, 9783036531861, 9783036531878
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Shrnutí:	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.
ISBN:	3036531874 3036531866 9783036531861 9783036531878
DOI:	10.3390/books978-3-0365-3187-8