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Coherence : In Signal Processing and Machine Learning / David Ramírez, Ignacio Santamaría, Louis Scharf
| Coherence : In Signal Processing and Machine Learning / David Ramírez, Ignacio Santamaría, Louis Scharf |
| Autore | Ramírez, David |
| Pubbl/distr/stampa | Cham, : Springer, 2022 |
| Descrizione fisica | xxi, 487 p. : ill. ; 24 cm |
| Altri autori (Persone) |
Santamaría, Ignacio
Scharf, Louis |
| Soggetto non controllato |
Beamforming and spectrum analysis
Canonical and multiset correlation analysis Coherence in compressed sensing Coherence in science and engineering Coherence in signal processing Correlation and partial correlation analysis Hypothesis testing for covariance structure Kernel methods Least squares and its applications Matched and adaptive subspace detectors Matrix optimization Multichannel coherence Multichannel detection of spacetime signals Multidimensional Scaling Normal and matrix distribution theory Passive and active detection Performance bounds and uncertainty quantification Principal component analysis Subspace averaging and its applications |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN0276976 |
Ramírez, David
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| Cham, : Springer, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Coherence : In Signal Processing and Machine Learning / David Ramírez, Ignacio Santamaría, Louis Scharf
| Coherence : In Signal Processing and Machine Learning / David Ramírez, Ignacio Santamaría, Louis Scharf |
| Autore | Ramírez, David |
| Pubbl/distr/stampa | Cham, : Springer, 2022 |
| Descrizione fisica | xxi, 487 p. : ill. ; 24 cm |
| Altri autori (Persone) |
Santamaría, Ignacio
Scharf, Louis |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
68-XX - Computer science [MSC 2020] 94-XX - Information and communication theory, circuits [MSC 2020] |
| Soggetto non controllato |
Beamforming and spectrum analysis
Canonical and multiset correlation analysis Coherence in compressed sensing Coherence in science and engineering Coherence in signal processing Correlation and partial correlation analysis Hypothesis testing for covariance structure Kernel methods Least squares and its applications Matched and adaptive subspace detectors Matrix optimization Multichannel coherence Multichannel detection of spacetime signals Multidimensional Scaling Normal and matrix distribution theory Passive and active detection Performance bounds and uncertainty quantification Principal component analysis Subspace averaging and its applications |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00276976 |
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Ramírez, David
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| Cham, : Springer, 2022 | ||
| 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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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]
62H25 - Factor analysis and principal components; correspondence analysis [MSC 2020] 62R07 - Statistical aspects of big data and data science [MSC 2020] 68T05 - Learning and adaptive systems in artificial intelligence [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-VAN00249280 |
| Cham, : Springer, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Kernel Mode Decomposition and the Programming of Kernels / Houman Owhadi, Clint Scovel, Gene Ryan Yoo
| Kernel Mode Decomposition and the Programming of Kernels / Houman Owhadi, Clint Scovel, Gene Ryan Yoo |
| Autore | Owhadi, Houman |
| Pubbl/distr/stampa | Cham, : Springer, 2021 |
| Descrizione fisica | x, 118 p. : ill. ; 24 cm |
| Altri autori (Persone) |
Scovel, Clint
Yoo, Gene Ryan |
| Soggetto topico |
68T10 - Pattern recognition, speech recognition [MSC 2020]
62J02 - General nonlinear regression [MSC 2020] 62-XX - Statistics [MSC 2020] 62G07 - Density estimation [MSC 2020] 62J12 - Generalized linear models (logistic models) [MSC 2020] 62R07 - Statistical aspects of big data and data science [MSC 2020] 62H30 - Classification and discrimination; cluster analysis (statistical aspects) [MSC 2020] 62G08 - Nonparametric regression and quantile regression [MSC 2020] 68T09 - Computational aspects of data analysis and big data [MSC 2020] |
| Soggetto non controllato |
Additive models
Empirical mode decomposition Gaussian process regression Kernel methods Time-frequency decomposition |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN0274859 |
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Owhadi, Houman
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| Cham, : Springer, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Kernel Mode Decomposition and the Programming of Kernels / Houman Owhadi, Clint Scovel, Gene Ryan Yoo
| Kernel Mode Decomposition and the Programming of Kernels / Houman Owhadi, Clint Scovel, Gene Ryan Yoo |
| Autore | Owhadi, Houman |
| Pubbl/distr/stampa | Cham, : Springer, 2021 |
| Descrizione fisica | x, 118 p. : ill. ; 24 cm |
| Altri autori (Persone) |
Scovel, Clint
Yoo, Gene Ryan |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
62G07 - Density estimation [MSC 2020] 62G08 - Nonparametric regression and quantile regression [MSC 2020] 62H30 - Classification and discrimination; cluster analysis (statistical aspects) [MSC 2020] 62J02 - General nonlinear regression [MSC 2020] 62J12 - Generalized linear models (logistic models) [MSC 2020] 62R07 - Statistical aspects of big data and data science [MSC 2020] 68T09 - Computational aspects of data analysis and big data [MSC 2020] 68T10 - Pattern recognition, speech recognition [MSC 2020] |
| Soggetto non controllato |
Additive models
Empirical mode decomposition Gaussian process regression Kernel methods Time-frequency decomposition |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00274859 |
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Owhadi, Houman
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| Cham, : Springer, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Machine Learning Paradigms : Advances in Deep Learning-based Technological Applications / George A. Tsihrintzis, Lakhmi C. Jain editors
| Machine Learning Paradigms : Advances in Deep Learning-based Technological Applications / George A. Tsihrintzis, Lakhmi C. Jain editors |
| Pubbl/distr/stampa | Cham, : Springer, 2020 |
| Descrizione fisica | xi, 430 p. : ill. ; 24 cm |
| Soggetto topico |
68Txx - Artificial intelligence [MSC 2020]
68T05 - Learning and adaptive systems in artificial intelligence [MSC 2020] |
| Soggetto non controllato |
Bioinformatics
Biosciences Computer Games Computer vision Deep Learning Networks Evolutionary Approaches Image and Speech Processing Kernel methods Natural Language Processing Neural networks Reinforcement Relational Learning Semi-supervised Supervised Unsupervised Virtual Environments |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0249412 |
| Cham, : Springer, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Machine Learning Paradigms : Advances in Deep Learning-based Technological Applications / George A. Tsihrintzis, Lakhmi C. Jain editors
| Machine Learning Paradigms : Advances in Deep Learning-based Technological Applications / George A. Tsihrintzis, Lakhmi C. Jain editors |
| Pubbl/distr/stampa | Cham, : Springer, 2020 |
| Descrizione fisica | xi, 430 p. : ill. ; 24 cm |
| Soggetto topico |
68T05 - Learning and adaptive systems in artificial intelligence [MSC 2020]
68Txx - Artificial intelligence [MSC 2020] |
| Soggetto non controllato |
Bioinformatics
Biosciences Computer Games Computer vision Deep Learning Networks Evolutionary Approaches Image and Speech Processing Kernel methods Natural language Neural networks Reinforcement Relational Learning Semi-supervised Supervised Unsupervised Virtual Environments |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN00249412 |
| Cham, : Springer, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Machine Learning with Quantum Computers / Maria Schuld, Francesco Petruccione
| Machine Learning with Quantum Computers / Maria Schuld, Francesco Petruccione |
| Autore | Schuld, Maria |
| Edizione | [2. ed] |
| Pubbl/distr/stampa | Cham, : Springer, 2021 |
| Descrizione fisica | xiv, 312 p. : ill. ; 24 cm |
| Altri autori (Persone) | Petruccione, Francesco |
| Soggetto topico |
68Q12 - Quantum algorithms and complexity in the theory of computing [MSC 2020]
68T05 - Learning and adaptive systems in artificial intelligence [MSC 2020] 81-XX - Quantum theory [MSC 2020] 81P68 - Quantum computation [MSC 2020] 82C32 - Neural nets applied to problems in time-dependent statistical mechanics [MSC 2020] |
| Soggetto non controllato |
Artificial neural network
Deep Belief Network Deutsch-Josza algorithm Grover search Hidden Markov models Hopfield Model Kernel methods Qsample encoding Quantum blas |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN00282609 |
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Schuld, Maria
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| Cham, : Springer, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Minimum Divergence Methods in Statistical Machine Learning : From an Information Geometric Viewpoint / Shinto Eguchi, Osamu Komori
| Minimum Divergence Methods in Statistical Machine Learning : From an Information Geometric Viewpoint / Shinto Eguchi, Osamu Komori |
| Autore | Eguchi, Shinto |
| Pubbl/distr/stampa | Tokyo, : Springer, 2022 |
| Descrizione fisica | x, 221 p. : ill. ; 24 cm |
| Altri autori (Persone) | Komori, Osamu |
| Soggetto non controllato |
Boosting
Independent component analysis Information Geometry Kernel methods Machine learning |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00278247 |
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Eguchi, Shinto
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| Tokyo, : Springer, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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