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Advances in Artificial Intelligence: Models, Optimization, and Machine Learning
| Advances in Artificial Intelligence: Models, Optimization, and Machine Learning |
| Autore | Leon Florin |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 online resource (362 p.) |
| Soggetto topico |
Mathematics and Science
Research and information: general |
| Soggetto non controllato |
NET framework
adaptive sampling agent algorithms agent-based systems animal-inspired applied machine learning approximate differential optimization autonomous driving class imbalance classification classification and regression combinatorics computational complexity data mining deep learning deep neural networks DeepFKTNet defect classification distance metrics distributed W-learning dynamic programming algorithm engineering informatics ensemble model evolutionary algorithm exploitation exploration free radical polymerization gender-based violence in Mexico generative adversarial networks graph neural network hot rolled strip steel hyperparameters image classification inference instance-based learning intelligent transport systems interoperability k-nearest neighbor knockout tournament large margin nearest neighbor regression machine learning metaheuristics multi-agent framework multi-agent systems multiple point hill climbing multisensory fingerprint n/a object tracking optimization plastic bottle prototypes reinforcement learning simulations software design spatial-temporal variable speed limit stochastic methods surface defects traffic control trajectory prediction transfer learning twitter messages urban motorways |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Advances in Artificial Intelligence |
| Record Nr. | UNINA-9910580212403321 |
Leon Florin
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Approximate Bayesian Inference
| Approximate Bayesian Inference |
| Autore | Alquier Pierre |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 online resource (508 p.) |
| Soggetto topico |
Mathematics and Science
Research and information: general |
| Soggetto non controllato |
approximate Bayesian computation
Approximate Bayesian Computation approximate Bayesian computation (ABC) Bayesian inference Bayesian sampling Bayesian statistics Bethe free energy bifurcation complex systems control variates data imputation data streams deep learning differential evolution differential privacy (DP) discrete state space dynamical systems Edward-Sokal coupling entropy ergodicity expectation-propagation factor graphs fixed-form variational Bayes Gaussian generalisation bounds Gibbs posterior gradient descent greedy algorithm Hamilton Monte Carlo hyperparameters integrated nested laplace approximation Kullback-Leibler divergence Langevin dynamics Langevin Monte Carlo Laplace approximations machine learning Markov chain Markov chain Monte Carlo Markov Chain Monte Carlo Markov kernels MCMC MCMC-SAEM mean-field message passing meta-learning Monte Carlo integration network modeling network variability neural networks no free lunch theorems non-reversible dynamics online learning online optimization PAC-Bayes PAC-Bayes theory particle flow principal curves priors probably approximately correct regret bounds Riemann Manifold Hamiltonian Monte Carlo robustness sequential learning sequential Monte Carlo Sequential Monte Carlo sleeping experts sparse vector technique (SVT) statistical learning theory statistical mechanics Stiefel manifold stochastic gradients stochastic volatility thinning variable flow variational approximations variational Bayes variational free energy variational inference variational message passing |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910576874903321 |
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Alquier Pierre
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Numerical Modeling in Civil and Mining Geotechnical Engineering
| Numerical Modeling in Civil and Mining Geotechnical Engineering |
| Autore | Li Li |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (300 p.) |
| Soggetto topico |
Technology: general issues
History of engineering & technology |
| Soggetto non controllato |
near-surface thick deposit
surface subsidence numerical simulation unmanned aerial survey accurate model railway ballast fouling ballast degradation micro-mechanical parameters shear strength large diameter bored pile hyperparameters supervised machine learning finite element method parametric study load transfer failure mechanism geothermal heat exchangers permafrost thaw settlement sustainability embedded beam elements finite element mesh sensitivity soil-structure interaction deep foundation 3D modelling fluid fine tailings dewatering modelling seasonal weathering freeze-thaw evaporation mining backfill compressibility constitutive models numerical modeling plasticity critical state soil model modified Cam Clay model model normalization precomputation incompatibility rock plastic deformation goaf-side entry stability of surrounding rock pillar size optimization confined water paste filling mining filling step advancing distance floor failure tailings dam impacting force kinetic energy smoothed particle hydrodynamics (SPH) 3D nonlinear yield criterion elastoplastic model circular opening backfill FLAC3D pore water pressure hydraulic conductivity alternative method numerical analyses unsaturated soil |
| ISBN | 3-0365-5442-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910674391503321 |
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Li Li
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| MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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