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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 electronic resource (362 p.) |
| Soggetto topico |
Research & information: general
Mathematics & science |
| Soggetto non controllato |
large margin nearest neighbor regression
distance metrics prototypes evolutionary algorithm approximate differential optimization multiple point hill climbing adaptive sampling free radical polymerization autonomous driving object tracking trajectory prediction deep neural networks stochastic methods applied machine learning classification and regression data mining ensemble model engineering informatics gender-based violence in Mexico twitter messages class imbalance k-nearest neighbor instance-based learning graph neural network deep learning hyperparameters machine learning optimization inference metaheuristics animal-inspired exploration exploitation hot rolled strip steel surface defects defect classification knockout tournament dynamic programming algorithm computational complexity combinatorics intelligent transport systems traffic control spatial-temporal variable speed limit multi-agent systems reinforcement learning distributed W-learning urban motorways multi-agent framework NET framework simulations agent-based systems agent algorithms software design multisensory fingerprint interoperability DeepFKTNet classification generative adversarial networks image classification transfer learning plastic bottle |
| 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 electronic resource (508 p.) |
| Soggetto topico |
Research & information: general
Mathematics & science |
| Soggetto non controllato |
bifurcation
dynamical systems Edward–Sokal coupling mean-field Kullback–Leibler divergence variational inference Bayesian statistics machine learning variational approximations PAC-Bayes expectation-propagation Markov chain Monte Carlo Langevin Monte Carlo sequential Monte Carlo Laplace approximations approximate Bayesian computation Gibbs posterior MCMC stochastic gradients neural networks Approximate Bayesian Computation differential evolution Markov kernels discrete state space ergodicity Markov chain probably approximately correct variational Bayes Bayesian inference Markov Chain Monte Carlo Sequential Monte Carlo Riemann Manifold Hamiltonian Monte Carlo integrated nested laplace approximation fixed-form variational Bayes stochastic volatility network modeling network variability Stiefel manifold MCMC-SAEM data imputation Bethe free energy factor graphs message passing variational free energy variational message passing approximate Bayesian computation (ABC) differential privacy (DP) sparse vector technique (SVT) Gaussian particle flow variable flow Langevin dynamics Hamilton Monte Carlo non-reversible dynamics control variates thinning meta-learning hyperparameters priors online learning online optimization gradient descent statistical learning theory PAC–Bayes theory deep learning generalisation bounds Bayesian sampling Monte Carlo integration PAC-Bayes theory no free lunch theorems sequential learning principal curves data streams regret bounds greedy algorithm sleeping experts entropy robustness statistical mechanics complex systems |
| 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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