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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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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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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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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 | ||
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Modern methodology and applications in spatial-temporal modeling / Gareth William Peters, Tomoko Matsui editors
| Modern methodology and applications in spatial-temporal modeling / Gareth William Peters, Tomoko Matsui editors |
| Pubbl/distr/stampa | Tokyo, : Springer, 2015 |
| Descrizione fisica | XV, 111 p. : ill. ; 24 cm |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
62Pxx - Applications of statistics [MSC 2020] 62M30 - Inference from spatial processes [MSC 2020] |
| Soggetto non controllato |
Audio and Music Signal Processing
Gaussian processes Kernel methods Non-Parametric Bayesian Inference Wireless Signal Processing |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0113976 |
| Tokyo, : Springer, 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Nonparametric statistics : 4. ISNPS, Salerno, Italy, June 2018 / Michele La Rocca, Brunero Liseo, Luigi Salmaso editors
| Nonparametric statistics : 4. ISNPS, Salerno, Italy, June 2018 / Michele La Rocca, Brunero Liseo, Luigi Salmaso editors |
| Pubbl/distr/stampa | Cham, : Springer, 2020 |
| Descrizione fisica | x, 547 p. : ill. ; 24 cm |
| Soggetto topico |
62Gxx - Nonparametric inference [MSC 2020]
62G05 - Nonparametric estimation [MSC 2020] 62G08 - Nonparametric regression and quantile regression [MSC 2020] 62G10 - Nonparametric hypothesis testing [MSC 2020] 62G35 - Nonparametric robustness [MSC 2020] 62G20 - Asymptotic properties of nonparametric inference [MSC 2020] 62G09 - Nonparametric statistical resampling methods [MSC 2020] 62G15 - Nonparametric tolerance and confidence regions [MSC 2020] |
| Soggetto non controllato |
Big Data
Dependent data Heavy-Tailed distribution High-Dimensional Data Kernel methods Machine learning Nonparametric Statistics Nonparametric inference Nonparametric smoother Resampling Statistical learning Survey sampling Time series |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0249540 |
| Cham, : Springer, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Nonparametric statistics : 3. ISNPS, Avignon, France, June 2016 / Patrice Bertail ... [et al.] editors
| Nonparametric statistics : 3. ISNPS, Avignon, France, June 2016 / Patrice Bertail ... [et al.] editors |
| Pubbl/distr/stampa | Cham, : Springer, 2018 |
| Descrizione fisica | ix, 390 p. : ill. ; 24 cm |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
62Gxx - Nonparametric inference [MSC 2020] |
| Soggetto non controllato |
Big Data
Dependent data Heavy-Tailed distribution High-Dimensional Data Kernel methods Machine learning Nonparametric Statistics Nonparametric inference Nonparametric smoother Resampling Statistical learning Survey sampling Time series |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0124900 |
| Cham, : Springer, 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Supervised Learning with Quantum Computers / Maria Schuld, Francesco Petruccione
| Supervised Learning with Quantum Computers / Maria Schuld, Francesco Petruccione |
| Autore | Schuld, Maria |
| Pubbl/distr/stampa | Cham, : Springer, 2018 |
| Descrizione fisica | xiii, 297 p. : ill. ; 24 cm |
| Altri autori (Persone) | Petruccione, Francesco |
| Soggetto topico |
81P68 - Quantum computation [MSC 2020]
68Qxx - Theory of computing [MSC 2020] 81-XX - Quantum theory [MSC 2020] 68T05 - Learning and adaptive systems in artificial intelligence [MSC 2020] 68Q12 - Quantum algorithms and complexity in the theory of computing [MSC 2020] 68Q32 - Computational learning theory [MSC 2020] 82C32 - Neural nets applied to problems in time-dependent statistical mechanics [MSC 2020] |
| Soggetto non controllato |
Adiabatic quantum computing
Artificial neural network Belief nets Boltzmann machines Data driven prediction Deutsch-Josza algorithm Grover search Hidden Markov Model Hopfield models Kernel methods Near term application Qsample encoding Quantum Walks Quantum annealing Quantum blas Quantum gates Quantum inference Quantum machine learning Quantum phase estimation |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0211752 |
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Schuld, Maria
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| Cham, : Springer, 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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