Computational Optimizations for Machine Learning

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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Bibliographic Details
Other Authors: Gabbay, Freddy (Editor)
Format: Book Chapter
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
Subjects:
Research & information: general
Mathematics & science
ARIMA model
time series analysis
online optimization
online model selection
precipitation nowcasting
deep learning
autoencoders
radar data
generalization error
recurrent neural networks
machine learning
model predictive control
nonlinear systems
neural networks
low power
quantization
CNN architecture
multi-objective optimization
genetic algorithms
evolutionary computation
swarm intelligence
Heating, Ventilation and Air Conditioning (HVAC)
metaheuristics search
bio-inspired algorithms
smart building
soft computing
training
evolution of weights
artificial intelligence
deep neural networks
convolutional neural network
deep compression
DNN
ReLU
floating-point numbers
hardware acceleration
energy dissipation
FLOW-3D
hydraulic jumps
bed roughness
sensitivity analysis
feature selection
evolutionary algorithms
nature inspired algorithms
meta-heuristic optimization
computational intelligence
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Description
Summary: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.
Physical Description:1 electronic resource (276 p.)
ISBN:books978-3-0365-3187-8
9783036531861
9783036531878
Access:Open Access

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