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3D Point Cloud Analysis : Traditional, Deep Learning, and Explainable Machine Learning Methods / Shan Liu ... [et al.]
| 3D Point Cloud Analysis : Traditional, Deep Learning, and Explainable Machine Learning Methods / Shan Liu ... [et al.] |
| Pubbl/distr/stampa | Cham, : Springer, 2021 |
| Descrizione fisica | xiv, 146 p. : ill. ; 24 cm |
| Soggetto topico |
68-XX - Computer science [MSC 2020]
68T05 - Learning and adaptive systems in artificial intelligence [MSC 2020] 68T07 - Artificial neural networks and deep learning [MSC 2020] 68T45 - Machine vision and scene understanding [MSC 2020] |
| Soggetto non controllato |
3D computer vision
3D object detection 3D object recognition Deep Learning Explainable machine learning Machine learning ModelNet40 Point cloud analysis Point cloud classification Point cloud part segmentation Point cloud registration PointHop R-PointHop Saab transform ShapeNet Successive subspace learning Unsupervised learning |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN0274505 |
| Cham, : Springer, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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3D Point Cloud Analysis : Traditional, Deep Learning, and Explainable Machine Learning Methods / Shan Liu ... [et al.]
| 3D Point Cloud Analysis : Traditional, Deep Learning, and Explainable Machine Learning Methods / Shan Liu ... [et al.] |
| Pubbl/distr/stampa | Cham, : Springer, 2021 |
| Descrizione fisica | xiv, 146 p. : ill. ; 24 cm |
| Soggetto topico |
68-XX - Computer science [MSC 2020]
68T05 - Learning and adaptive systems in artificial intelligence [MSC 2020] 68T07 - Artificial neural networks and deep learning [MSC 2020] 68T45 - Machine vision and scene understanding [MSC 2020] |
| Soggetto non controllato |
3D computer vision
3D object detection 3D object recognition Deep Learning Explainable machine learning Machine learning ModelNet40 Point cloud analysis Point cloud classification Point cloud part segmentation Point cloud registration PointHop R-PointHop Saab transform ShapeNet Successive subspace learning Unsupervised learning |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00274505 |
| Cham, : Springer, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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A matrix algebra approach to artificial intelligence / Xian-Da Zhang
| A matrix algebra approach to artificial intelligence / Xian-Da Zhang |
| Autore | Zhang, Xian-Da |
| Pubbl/distr/stampa | Singapore, : Springer, 2020 |
| Descrizione fisica | xxxiv, 820 p. : ill. ; 25 cm |
| Soggetto topico |
68-XX - Computer science [MSC 2020]
68T01 - General topics in artificial intelligence [MSC 2020] 68T05 - Learning and adaptive systems in artificial intelligence [MSC 2020] 68T07 - Artificial neural networks and deep learning [MSC 2020] 68T20 - Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) [MSC 2020] 90C25 - Convex programming [MSC 2020] 90C59 - Approximation methods and heuristics in mathematical programming [MSC 2020] |
| ISBN | 978-98-11-52769-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00298969 |
Zhang, Xian-Da
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| Singapore, : Springer, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Artificial Intelligence for Scientific Discoveries : Extracting Physical Concepts from Experimental Data Using Deep Learning / Raban Iten
| Artificial Intelligence for Scientific Discoveries : Extracting Physical Concepts from Experimental Data Using Deep Learning / Raban Iten |
| Autore | Iten, Raban |
| Pubbl/distr/stampa | Cham, : Springer, 2023 |
| Descrizione fisica | xiii, 170 p. : ill. ; 24 cm |
| Soggetto topico |
00A79 (77-XX) - Physics [MSC 2020]
68-XX - Computer science [MSC 2020] 68T07 - Artificial neural networks and deep learning [MSC 2020] 68Txx - Artificial intelligence [MSC 2020] |
| Soggetto non controllato |
AI-Scientist
Artificial Intelligence Automation of Physics Deep Learning Discovering Physical Laws Extracting Equations from Data Heliocentric Solar System Machine learning Neural networks Representation Learning |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00284817 |
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Iten, Raban
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| Cham, : Springer, 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Artificial Neural Networks and Structural Equation Modeling : Marketing and Consumer Research Applications / Alhamzah Alnoor, Khaw Khai Wah, Azizul Hassan editors
| Artificial Neural Networks and Structural Equation Modeling : Marketing and Consumer Research Applications / Alhamzah Alnoor, Khaw Khai Wah, Azizul Hassan editors |
| Pubbl/distr/stampa | Singapore, : Springer, 2022 |
| Descrizione fisica | ix, 341 p. : ill. ; 24 cm |
| Soggetto topico |
68T07 - Artificial neural networks and deep learning [MSC 2020]
90B60 - Marketing, advertising [MSC 2020] |
| Soggetto non controllato |
Artificial neural network for consumer research
Social commerce and technology Structural equation modelling for consumer research Sustainability Technology acceptance in marketing and consumer research |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00278339 |
| Singapore, : Springer, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Bayesian Nonparametric Statistics : École d’Été de Probabilités de Saint-Flour LI - 2023 / Ismaël Castillo
| Bayesian Nonparametric Statistics : École d’Été de Probabilités de Saint-Flour LI - 2023 / Ismaël Castillo |
| Autore | Castillo, Ismaël |
| Pubbl/distr/stampa | Cham, : Springer, 2024 |
| Descrizione fisica | xii, 216 p. : ill. ; 24 cm |
| Soggetto topico |
60-XX - Probability theory and stochastic processes [MSC 2020]
62-XX - Statistics [MSC 2020] 62C10 - Bayesian problems; characterization of Bayes procedures [MSC 2020] 68T07 - Artificial neural networks and deep learning [MSC 2020] |
| Soggetto non controllato |
Bayesian Deep Neural Networks
Bayesian Inference Bernstein-von Mises Theorems High-Dimensional Models Nonparametric Models Posterior distributions Uncertainty Quantification Variational Bayes |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00286238 |
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Castillo, Ismaël
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| Cham, : Springer, 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Chemical Master Equation for Large Biological Networks : State-space Expansion Methods Using AI / Don Kulasiri, Rahul Kosarwal
| Chemical Master Equation for Large Biological Networks : State-space Expansion Methods Using AI / Don Kulasiri, Rahul Kosarwal |
| Autore | Kulasiri, Don |
| Pubbl/distr/stampa | Singapore, : Springer, 2021 |
| Descrizione fisica | xviii, 217 p. : ill. ; 24 cm |
| Altri autori (Persone) | Kosarwal, Rahul |
| Soggetto topico |
60J28 - Applications of continuous-time Markov processes on discrete state spaces [MSC 2020]
68T07 - Artificial neural networks and deep learning [MSC 2020] 92-XX - Biology and other natural sciences [MSC 2020] 92C40 - Biochemistry, molecular biology [MSC 2020] 92C42 - Systems biology, networks [MSC 2020] 92C45 - Kinetics in biochemical problems (pharmacokinetics, enzyme kinetics, etc.) [MSC 2020] |
| Soggetto non controllato |
Artificial Intelligence
Bayesian methods Biochemical Networks Bionetworks Markov Processes Markov graphs Markov tree Model Building Modeling and integration Numerical simulations |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00283061 |
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Kulasiri, Don
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| Singapore, : Springer, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Complex-Valued Neural Networks Systems with Time Delay : Stability Analysis and (Anti-)Synchronization Control / Ziye Zhang ... [et al.]
| Complex-Valued Neural Networks Systems with Time Delay : Stability Analysis and (Anti-)Synchronization Control / Ziye Zhang ... [et al.] |
| Pubbl/distr/stampa | Singapore, : Springer, 2022 |
| Descrizione fisica | xi, 229 p. : ill. ; 24 cm |
| Soggetto topico |
68T07 - Artificial neural networks and deep learning [MSC 2020]
93-XX - Systems theory; control [MSC 2020] 93B70 - Networked control [MSC 2020] 93C43 - Delay control/observation systems [MSC 2020] 93D23 - Exponential stability [MSC 2020] 93D40 - Finite-time stability [MSC 2020] |
| Soggetto non controllato |
Adaptive synchronization
Anti-synchronization control Asymptotic stability Complex-valued neural networks Finite-time stability Finite-time synchronization Finite/fixed-time synchronization Fixed-time synchronization Hopf bifurcation Lagrange exponential stability Pinning synchronization Time Delay systems analysis |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00284026 |
| Singapore, : Springer, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Computational Reconstruction of Missing Data in Biological Research / Feng Bao
| Computational Reconstruction of Missing Data in Biological Research / Feng Bao |
| Autore | Bao, Feng |
| Pubbl/distr/stampa | Singapore, : Springer, 2021 |
| Descrizione fisica | xvii, 105 p. : ill. ; 24 cm |
| Soggetto topico |
92C37 - Cell biology [MSC 2020]
92C55 - Biomedical imaging and signal processing [MSC 2020] 62-XX - Statistics [MSC 2020] 92D10 - Genetics and epigenetics [MSC 2020] 62P10 - Applications of statistics to biology and medical sciences; meta analysis [MSC 2020] 68T07 - Artificial neural networks and deep learning [MSC 2020] 62D10 - Missing data [MSC 2020] |
| Soggetto non controllato |
Biological data analysis
Data imputation Imbalance learning Machine learning Single Cell Analysis |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN0275440 |
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Bao, Feng
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| Singapore, : Springer, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Computational Reconstruction of Missing Data in Biological Research / Feng Bao
| Computational Reconstruction of Missing Data in Biological Research / Feng Bao |
| Autore | Bao, Feng |
| Pubbl/distr/stampa | Singapore, : Springer, 2021 |
| Descrizione fisica | xvii, 105 p. : ill. ; 24 cm |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
62D10 - Missing data [MSC 2020] 62P10 - Applications of statistics to biology and medical sciences; meta analysis [MSC 2020] 68T07 - Artificial neural networks and deep learning [MSC 2020] 92C37 - Cell biology [MSC 2020] 92C55 - Biomedical imaging and signal processing [MSC 2020] 92D10 - Genetics and epigenetics [MSC 2020] |
| Soggetto non controllato |
Biological data analysis
Data imputation Imbalance learning Machine learning Single Cell Analysis |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00275440 |
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Bao, Feng
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| Singapore, : Springer, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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