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Advanced Data Analysis in Neuroscience : Integrating Statistical and Computational Models / Daniel Durstewitz
| Advanced Data Analysis in Neuroscience : Integrating Statistical and Computational Models / Daniel Durstewitz |
| Autore | Durstewitz, Daniel |
| Pubbl/distr/stampa | Cham, : Springer, 2017 |
| Descrizione fisica | xxv, 292 p. : ill. ; 24 cm |
| Soggetto topico |
92C20 - Neural biology [MSC 2020]
62R07 - Statistical aspects of big data and data science [MSC 2020] 68T09 - Computational aspects of data analysis and big data [MSC 2020] |
| Soggetto non controllato |
Bootstrap methods
Change point analysis Clustering Dimensionality reduction Machine learning Multiple testing Multivariate maps and recurrent neural networks Multivariate statistics Neural time series Nonlinear dynamical systems Nonlinear oscillations Nonparametric time series modeling Principal component analysis Reconstructing state spaces from experimental data Statistical methods in neuroscience Statistical parameter estimation Unsupervised clustering |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0123545 |
Durstewitz, Daniel
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| Cham, : Springer, 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Compressed sensing and its applications : Second International MATHEON Conference 2015 / Holger Boche ... [et al.] editors
| Compressed sensing and its applications : Second International MATHEON Conference 2015 / Holger Boche ... [et al.] editors |
| Pubbl/distr/stampa | Cham, : Birkhäuser, 2017 |
| Descrizione fisica | xix, 388 p. : ill. ; 24 cm |
| Soggetto topico |
90C25 - Convex programming [MSC 2020]
68U10 - Computing methodologies for image processing [MSC 2020] 15B52 - Random matrices (algebraic aspects) [MSC 2020] 94A12 - Signal theory (characterization, reconstruction, filtering, etc.) [MSC 2020] 94A20 - Sampling theory in information and communication theory [MSC 2020] |
| Soggetto non controllato |
Compressed Sensing
Dimensionality reduction Fourier phase retrieval Hilbert spaces Information Theory Information and communication, circuits Matrix theory Random matrices Sparse approximation Sparse probability measures Sparse recovery Stochastic block model |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0124337 |
| Cham, : Birkhäuser, 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Euclidean Distance Geometry : an introduction / Leo Liberti, Carlile Lavor
| Euclidean Distance Geometry : an introduction / Leo Liberti, Carlile Lavor |
| Autore | Liberti, Leo |
| Pubbl/distr/stampa | Cham, : Springer, 2017 |
| Descrizione fisica | xiii, 133 p. : ill. ; 24 cm |
| Altri autori (Persone) | Lavor, Carlile |
| Soggetto topico | 51Kxx - Distance geometry [MSC 2020] |
| Soggetto non controllato |
Branch-and-Prune algorithm
Clock synchronization Complete graphs Convex Optimization Dimensionality reduction Discretizability Graph visualization Isomap K-laterative graphs Localization of sensor networks Mathematica Mathematical programming Multidimensional Scaling Unmanned underwater vehicles Vertex orders Weighted adjacency matrix |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0123996 |
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Liberti, Leo
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| Cham, : Springer, 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Fundamentals of Data Analytics : With a View to Machine Learning / Rudolf Mathar ... [et al.]
| Fundamentals of Data Analytics : With a View to Machine Learning / Rudolf Mathar ... [et al.] |
| Pubbl/distr/stampa | Cham, : Springer, 2020 |
| Descrizione fisica | xi, 127 p. : ill. ; 24 cm |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
68T05 - Learning and adaptive systems in artificial intelligence [MSC 2020] 62H25 - Factor analysis and principal components; correspondence analysis [MSC 2020] 62R07 - Statistical aspects of big data and data science [MSC 2020] |
| Soggetto non controllato |
Artificial Intelligence
Classification Clustering Data science Diffusion maps Dimensionality reduction Isomap Kernel methods Machine learning MapReduce Markov decision processes Matrix optimization and approximation Multidimensional Scaling Principal component analysis Spectral clustering Supervised machine learning Support Vector Machines Unsupervised machine learning |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0249280 |
| Cham, : Springer, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Mathematical Foundations for Data Analysis / Jeff M. Phillips
| Mathematical Foundations for Data Analysis / Jeff M. Phillips |
| Autore | Phillips, Jeff M. |
| Pubbl/distr/stampa | Cham, : Springer, 2021 |
| Descrizione fisica | xvii, 287 p. : ill. ; 24 cm |
| Soggetto topico |
68-XX - Computer science [MSC 2020]
62-XX - Statistics [MSC 2020] 62Hxx - Multivariate analysis [MSC 2020] 62R07 - Statistical aspects of big data and data science [MSC 2020] 68T09 - Computational aspects of data analysis and big data [MSC 2020] |
| Soggetto non controllato |
Big Data
Clustering Data Mining Data Sciences Data analysis Dimensionality reduction Geometry of Data Machine learning Neural networks Probability |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN0274930 |
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Phillips, Jeff M.
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| Cham, : Springer, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Time Series Analysis and Forecasting : Selected Contributions from ITISE 2017 / Ignacio Rojas, Héctor Pomares, Olga Valenzuela editors
| Time Series Analysis and Forecasting : Selected Contributions from ITISE 2017 / Ignacio Rojas, Héctor Pomares, Olga Valenzuela editors |
| Pubbl/distr/stampa | Cham, : Springer, 2018 |
| Descrizione fisica | xiii, 340 p. : ill. ; 24 cm |
| Soggetto topico |
68-XX - Computer science [MSC 2020]
58-XX - Global analysis, analysis on manifolds [MSC 2020] 37-XX - Dynamical systems and ergodic theory [MSC 2020] 60-XX - Probability theory and stochastic processes [MSC 2020] 62-XX - Statistics [MSC 2020] |
| Soggetto non controllato |
Artificial Intelligence
Big Data Complex data Computational intelligence methods Dimensionality reduction Econometric models Energy time series forecasting Forecasting Forecasting in real problems High-Dimensional Data Mathematical methodology for time series On-line learning in time series Pattern recognition Similarity measures Time series |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0125060 |
| Cham, : Springer, 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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