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...
محفوظ في:
| التنسيق: | Online |
|---|---|
| اللغة: | الإنجليزية |
| منشور في: |
MDPI - Multidisciplinary Digital Publishing Institute
2022
|
| الموضوعات: | |
| الوصول للمادة أونلاين: | ONIX_20220321_9783036531861_69 |
| الوسوم: |
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1869518606871560192 |
|---|---|
| collection | Directory of Open Access Books |
| description | 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 | Online |
| id | doab-20.500.12854ir-79633 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-796332024-03-28T03:32:17Z Computational Optimizations for Machine Learning Gabbay, Freddy 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 thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science 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. 2022-03-21T16:28:50Z 2022-03-21T16:28:50Z 2022 book ONIX_20220321_9783036531861_69 9783036531861 9783036531878 https://directory.doabooks.org/handle/20.500.12854/79633 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/5018 https://mdpi.com/books/pdfview/book/5018 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-3187-8 10.3390/books978-3-0365-3187-8 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036531861 9783036531878 276 Basel open access |
| spellingShingle | 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 thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science Computational Optimizations for Machine Learning |
| title | Computational Optimizations for Machine Learning |
| title_full | Computational Optimizations for Machine Learning |
| title_fullStr | Computational Optimizations for Machine Learning |
| title_full_unstemmed | Computational Optimizations for Machine Learning |
| title_short | Computational Optimizations for Machine Learning |
| title_sort | computational optimizations for machine learning |
| topic | 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 thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science |
| topic_facet | 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 thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science |
| url | ONIX_20220321_9783036531861_69 |