Reprint

Computational Optimizations for Machine Learning

Edited by
February 2022
276 pages
  • ISBN978-3-0365-3186-1 (Hardback)
  • ISBN978-3-0365-3187-8 (PDF)

This is a Reprint of the Special Issue Computational Optimizations for Machine Learning that was published in

Computer Science & Mathematics
Engineering
Physical Sciences
Public Health & Healthcare
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.

Format
  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
Keywords
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; deep learning; neural networks; artificial intelligence; machine learning; deep neural networks; convolutional neural network; deep compression; DNN; ReLU; floating-point numbers; hardware acceleration; artificial intelligence; energy dissipation; FLOW-3D; hydraulic jumps; bed roughness; sensitivity analysis; feature selection; evolutionary algorithms; nature inspired algorithms; meta-heuristic optimization; computational intelligence; soft computing