Computational Optimizations for Machine Learning
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| Author: |
Gabbay Freddy
|
| Title: |
Computational Optimizations for Machine Learning
|
| Publisher: | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Physical description: | 1 online resource (276 p.) |
| Topical subject: | Mathematics & science |
| Research & information: general | |
| Uncontrolled subject: | 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, Ventilation and Air Conditioning (HVAC) | |
| hydraulic jumps | |
| low power | |
| machine learning | |
| 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 | |
| ReLU | |
| sensitivity analysis | |
| smart building | |
| soft computing | |
| swarm intelligence | |
| time series analysis | |
| training | |
| Person (second resp.): | GabbayFreddy |
| Summary, etc: | 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. |
| Preferred title for the work: | Computational Optimizations for Machine Learning |
| Format: | Language material  |
| Bibliographic level | Monograph |
| Language: | English |
| Record Nr.: | 9910557610303321 |
| You will find it: | Univ. Federico II |
| Opac: | Check copies here |