Dimensionality Reduction in Data Science / Max Garzon ... [et al.] editors |
Pubbl/distr/stampa | Cham, : Springer, 2022 |
Descrizione fisica | xi, 265 p. : ill. ; 24 cm |
Soggetto non controllato |
Classification/Prediction
Cross-validation DNA hybridization Data Visualization Data science platforms Deep learning/Backpropagation Dimensionality reduction Experimental control Explainable solutions Extreme Dimensionality reduction Geometric methods Independent component analysis Information-theoretic methods Machine learning Molecular Methods No-Free-Lunch (NFL) theorem Regression methods Semi/Unsupervised learning Statistical Methods Supervised learning |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNICAMPANIA-VAN0277281 |
Cham, : Springer, 2022 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Vanvitelli | ||
|
Dimensionality Reduction in Data Science / Max Garzon ... [et al.] editors |
Pubbl/distr/stampa | Cham, : Springer, 2022 |
Descrizione fisica | xi, 265 p. : ill. ; 24 cm |
Soggetto topico |
00B15 - Collections of articles of miscellaneous specific interest [MSC 2020]
68-XX - Computer science [MSC 2020] 68T09 - Computational aspects of data analysis and big data [MSC 2020] |
Soggetto non controllato |
Classification/Prediction
Cross-validation DNA hybridization Data Visualization Data science platforms Deep learning/Backpropagation Dimensionality reduction Experimental control Explainable solutions Extreme Dimensionality reduction Geometric methods Independent component analysis Information-theoretic methods Machine learning Molecular Methods No-Free-Lunch (NFL) theorem Regression methods Semi/Unsupervised learning Statistical Methods Supervised learning |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNICAMPANIA-VAN00277281 |
Cham, : Springer, 2022 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Vanvitelli | ||
|
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 Method Machine learning |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNICAMPANIA-VAN0278247 |
Eguchi, Shinto | ||
Tokyo, : Springer, 2022 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Vanvitelli | ||
|
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 Method Machine learning |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNICAMPANIA-VAN00278247 |
Eguchi, Shinto | ||
Tokyo, : Springer, 2022 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Vanvitelli | ||
|