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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Format: E-Book
Sprache:Englisch
Veröffentlicht: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
Schlagworte:
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
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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