top

  Info

  • Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.

  Info

  • Utilizzare questo link per rimuovere la selezione effettuata.
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
Opac: Controlla la disponibilità qui
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
Opac: Controlla la disponibilità qui
An introduction to statistical learning : with applications in R / Gareth James ... [et al.]
An introduction to statistical learning : with applications in R / Gareth James ... [et al.]
Edizione [2. ed]
Pubbl/distr/stampa New York, : Springer, 2021
Descrizione fisica xv, 607 p. : ill. ; 25 cm
Soggetto topico 62-XX - Statistics [MSC 2020]
68T05 - Learning and adaptive systems in artificial intelligence [MSC 2020]
62Cxx - Statistical decision theory [MSC 2020]
Soggetto non controllato Data Mining
Inference
R software
Statistical learning
Supervised learning
Unsupervised learning
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNICAMPANIA-VAN0275551
New York, : Springer, 2021
Materiale a stampa
Lo trovi qui: Univ. Vanvitelli
Opac: Controlla la disponibilità qui
An introduction to statistical learning : with applications in R / Gareth James ... [et al.]
An introduction to statistical learning : with applications in R / Gareth James ... [et al.]
Edizione [2. ed]
Pubbl/distr/stampa New York, : Springer, 2021
Descrizione fisica xv, 607 p. : ill. ; 25 cm
Soggetto topico 62-XX - Statistics [MSC 2020]
62Cxx - Statistical decision theory [MSC 2020]
68T05 - Learning and adaptive systems in artificial intelligence [MSC 2020]
Soggetto non controllato Data Mining
Inference
R software
Statistical learning
Supervised learning
Unsupervised learning
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNICAMPANIA-VAN00275551
New York, : Springer, 2021
Materiale a stampa
Lo trovi qui: Univ. Vanvitelli
Opac: Controlla la disponibilità qui
Marginal and Functional Quantization of Stochastic Processes / Harald Luschgy, Gilles Pagès
Marginal and Functional Quantization of Stochastic Processes / Harald Luschgy, Gilles Pagès
Autore Luschgy, Harald
Pubbl/distr/stampa Cham, : Springer, 2023
Descrizione fisica xviii, 912 p. : ill. ; 24 cm
Altri autori (Persone) Pagès, Gilles
Soggetto non controllato Cluster algorithms
Continuous-time stochastic processes
Discretization Methods
Luschgy Graf
Numerical Probability
Numerical methods in probability
Pages Numerical Probability
Random vectors
Signal processing
Signal transmission
Space discretizations
Unsupervised learning
Vector Quantization
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNICAMPANIA-VAN0279517
Luschgy, Harald  
Cham, : Springer, 2023
Materiale a stampa
Lo trovi qui: Univ. Vanvitelli
Opac: Controlla la disponibilità qui
Marginal and Functional Quantization of Stochastic Processes / Harald Luschgy, Gilles Pagès
Marginal and Functional Quantization of Stochastic Processes / Harald Luschgy, Gilles Pagès
Autore Luschgy, Harald
Pubbl/distr/stampa Cham, : Springer, 2023
Descrizione fisica xviii, 912 p. : ill. ; 24 cm
Altri autori (Persone) Pagès, Gilles
Soggetto topico 46N30 - Applications of functional analysis in probability theory and statistics [MSC 2020]
60-XX - Probability theory and stochastic processes [MSC 2020]
60B11 - Probability theory on linear topological spaces [MSC 2020]
60G15 - Gaussian processes [MSC 2020]
65C30 - Numerical solutions to stochastic differential and integral equations [MSC 2020]
Soggetto non controllato Cluster algorithms
Continuous-time stochastic processes
Discretization Methods
Luschgy Graf
Numerical Probability
Numerical methods in probability
Pages Numerical Probability
Random vectors
Signal processing
Signal transmission
Space discretizations
Unsupervised learning
Vector Quantization
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNICAMPANIA-VAN00279517
Luschgy, Harald  
Cham, : Springer, 2023
Materiale a stampa
Lo trovi qui: Univ. Vanvitelli
Opac: Controlla la disponibilità qui
Neural Networks and Deep Learning : A Textbook / Charu C. Aggarwal
Neural Networks and Deep Learning : A Textbook / Charu C. Aggarwal
Autore Aggarwal, Charu C.
Edizione [2. ed]
Pubbl/distr/stampa Cham, : Springer, 2023
Descrizione fisica xxiv, 529 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]
82C32 - Neural nets applied to problems in time-dependent statistical mechanics [MSC 2020]
92B20 - Neural networks for/in biological studies, artificial life and related topics [MSC 2020]
Soggetto non controllato Adversarial learning
Artificial Intelligence
Deep Learning
Image Convolutional Networks
Machine learning
Neural networks
Pre-Trained Language Models
Recurrent Neural Networks
Reinforcement Learning
Transformers
Unsupervised learning
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Titolo uniforme
Record Nr. UNICAMPANIA-VAN00278913
Aggarwal, Charu C.  
Cham, : Springer, 2023
Materiale a stampa
Lo trovi qui: Univ. Vanvitelli
Opac: Controlla la disponibilità qui
Statistical Mechanics of Neural Networks / Haiping Huang
Statistical Mechanics of Neural Networks / Haiping Huang
Autore Huang, Haiping
Pubbl/distr/stampa Singapore, : Springer, : Higher Education, 2021
Descrizione fisica xviii, 296 p. : ill. ; 24 cm
Soggetto non controllato Cavity Method
Hopfield Model
Mean field theory
Random matrices
Replica Method
Restricted Boltzmann Machine
Unsupervised learning
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNICAMPANIA-VAN00283198
Huang, Haiping  
Singapore, : Springer, : Higher Education, 2021
Materiale a stampa
Lo trovi qui: Univ. Vanvitelli
Opac: Controlla la disponibilità qui