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Deep Learning Applications with Practical Measured Results in Electronics Industries
| Deep Learning Applications with Practical Measured Results in Electronics Industries |
| Autore | Kung Hsu-Yang |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 online resource (272 p.) |
| Soggetto topico | History of engineering and technology |
| Soggetto non controllato |
A*
background model binary classification CNN compressed sensing computational intelligence content reconstruction convolutional network data fusion data partition deep learning digital shearography discrete wavelet transform dot grid target eye-tracking device faster region-based CNN forecasting foreign object GA gated recurrent unit generative adversarial network geometric errors geometric errors correction GSA-BP human computer interaction humidity sensor hyperspectral image classification image compression image inpainting image restoration imaging confocal microscope Imaging Confocal Microscope information measure instance segmentation intelligent surveillance intelligent tire manufacturing K-means clustering kinematic modelling lateral stage errors Least Squares method long short-term memory machine learning MCM uncertainty evaluation multiple constraints multiple linear regression multivariate temporal convolutional network multivariate time series forecasting neighborhood noise reduction network layer contribution neural audio caption neural networks neuro-fuzzy systems nonlinear optimization offshore wind optimization techniques oral evaluation recommender system reinforcement learning residual networks rigid body kinematics saliency information smart grid supervised learning tire bubble defects tire quality assessment trajectory planning transfer learning UAV underground mines unmanned aerial vehicle unsupervised learning update mechanism update occasion visual tracking |
| ISBN | 3-03928-864-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910404080403321 |
Kung Hsu-Yang
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| MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Information Theory and Machine Learning
| Information Theory and Machine Learning |
| Autore | Zheng Lizhong |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (254 p.) |
| Soggetto topico |
Technology: general issues
History of engineering & technology |
| Soggetto non controllato |
supervised classification
independent and non-identically distributed features analytical error probability empirical risk generalization error K-means clustering model compression population risk rate distortion theory vector quantization overfitting information criteria entropy model-based clustering merging mixture components component overlap interpretability time series prediction finite state machines hidden Markov models recurrent neural networks reservoir computers long short-term memory deep neural network information theory local information geometry feature extraction spiking neural network meta-learning information theoretic learning minimum error entropy artificial general intelligence closed-loop transcription linear discriminative representation rate reduction minimax game fairness HGR maximal correlation independence criterion separation criterion pattern dictionary atypicality Lempel–Ziv algorithm lossless compression anomaly detection information-theoretic bounds distribution and federated learning |
| ISBN | 3-0365-5308-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910619463403321 |
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Zheng Lizhong
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| MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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