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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
Main Author: Gabbay, Freddy
Format: eBook
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
Subjects:
Time Series Analysis
Machine Learning
Dnn
Hardware Acceleration
Soft Computing
Meta-heuristic Optimization
Mathematics & Science / Bicssc
Evolutionary Computation
Online Optimization
Bio-inspired Algorithms
Evolution Of Weights
Relu
Nonlinear Systems
Feature Selection
Smart Building
Precipitation Nowcasting
Deep Learning
Neural Networks
Deep Neural Networks
Swarm Intelligence
Low Power
Energy Dissipation
Online Model Selection
Quantization
Multi-objective Optimization
Model Predictive Control
Evolutionary Algorithms
Bed Roughness
Radar Data
Research & Information: General / Bicssc
Recurrent Neural Networks
Training
Sensitivity Analysis
Genetic Algorithms
Deep Compression
Hydraulic Jumps
Floating-point Numbers
Heating, Ventilation And Air Conditioning (hvac)
Artificial Intelligence
Computational Intelligence
Metaheuristics Search
Autoencoders
Convolutional Neural Network
Cnn Architecture
Flow-3d
Generalization Error
Nature Inspired Algorithms
Arima Model
Online Access:
Collection: Directory of Open Access Books - Collection details see MPG.ReNa
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.
Item Description:Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
Physical Description:1 electronic resource (276 p.)
ISBN:9783036531878
books978-3-0365-3187-8
9783036531861

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