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Machine learning and knowledge discovery in databases : European conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020 : proceedings, Parts I-V / / Yuxiao Dong, Dunja Mladenic, Craig Saunders
| Machine learning and knowledge discovery in databases : European conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020 : proceedings, Parts I-V / / Yuxiao Dong, Dunja Mladenic, Craig Saunders |
| Autore | Dong Yuxiao |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (612 pages) |
| Disciplina | 006.31 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Machine learning
Data mining Data structures (Computer science) |
| ISBN | 3-030-67667-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910484810703321 |
Dong Yuxiao
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine learning and knowledge discovery in databases : European conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020 : proceedings, Parts I-V / / Yuxiao Dong, Dunja Mladenic, Craig Saunders
| Machine learning and knowledge discovery in databases : European conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020 : proceedings, Parts I-V / / Yuxiao Dong, Dunja Mladenic, Craig Saunders |
| Autore | Dong Yuxiao |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (612 pages) |
| Disciplina | 006.31 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Machine learning
Data mining Data structures (Computer science) |
| ISBN | 3-030-67667-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996464441703316 |
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Dong Yuxiao
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track [[electronic resource] ] : European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part V / / edited by Yuxiao Dong, Georgiana Ifrim, Dunja Mladenić, Craig Saunders, Sofie Van Hoecke
| Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track [[electronic resource] ] : European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part V / / edited by Yuxiao Dong, Georgiana Ifrim, Dunja Mladenić, Craig Saunders, Sofie Van Hoecke |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (XLII, 577 p. 205 illus., 181 illus. in color.) |
| Disciplina | 006.312 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Data mining
Machine learning Education—Data processing Social sciences—Data processing Computer engineering Computer networks Data Mining and Knowledge Discovery Machine Learning Computers and Education Computer Application in Social and Behavioral Sciences Computer Engineering and Networks |
| ISBN | 3-030-67670-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Applied data science: recommendation -- applied data science: anomaly detection -- applied data science: Web mining -- applied data science: transportation -- applied data science: activity recognition -- applied data science: hardware and manufacturing -- applied data science: spatiotemporal data. |
| Record Nr. | UNISA-996464444203316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track : European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part V / / edited by Yuxiao Dong, Georgiana Ifrim, Dunja Mladenić, Craig Saunders, Sofie Van Hoecke
| Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track : European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part V / / edited by Yuxiao Dong, Georgiana Ifrim, Dunja Mladenić, Craig Saunders, Sofie Van Hoecke |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (XLII, 577 p. 205 illus., 181 illus. in color.) |
| Disciplina | 006.312 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Data mining
Machine learning Education—Data processing Social sciences—Data processing Computer engineering Computer networks Data Mining and Knowledge Discovery Machine Learning Computers and Education Computer Application in Social and Behavioral Sciences Computer Engineering and Networks |
| ISBN | 3-030-67670-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Applied data science: recommendation -- applied data science: anomaly detection -- applied data science: Web mining -- applied data science: transportation -- applied data science: activity recognition -- applied data science: hardware and manufacturing -- applied data science: spatiotemporal data. |
| Record Nr. | UNINA-9910484810503321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Subspace, Latent Structure and Feature Selection [[electronic resource] ] : Statistical and Optimization Perspectives Workshop, SLSFS 2005 Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers / / edited by Craig Saunders, Marko Grobelnik, Steve Gunn, John Shawe-Taylor
| Subspace, Latent Structure and Feature Selection [[electronic resource] ] : Statistical and Optimization Perspectives Workshop, SLSFS 2005 Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers / / edited by Craig Saunders, Marko Grobelnik, Steve Gunn, John Shawe-Taylor |
| Edizione | [1st ed. 2006.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2006 |
| Descrizione fisica | 1 online resource (X, 209 p.) |
| Disciplina | 003/.1 |
| Collana | Theoretical Computer Science and General Issues |
| Soggetto topico |
Algorithms
Computer science—Mathematics Mathematical statistics Computer science Artificial intelligence Computer vision Pattern recognition systems Probability and Statistics in Computer Science Theory of Computation Artificial Intelligence Computer Vision Automated Pattern Recognition |
| ISBN | 3-540-34138-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Invited Contributions -- Discrete Component Analysis -- Overview and Recent Advances in Partial Least Squares -- Random Projection, Margins, Kernels, and Feature-Selection -- Some Aspects of Latent Structure Analysis -- Feature Selection for Dimensionality Reduction -- Contributed Papers -- Auxiliary Variational Information Maximization for Dimensionality Reduction -- Constructing Visual Models with a Latent Space Approach -- Is Feature Selection Still Necessary? -- Class-Specific Subspace Discriminant Analysis for High-Dimensional Data -- Incorporating Constraints and Prior Knowledge into Factorization Algorithms – An Application to 3D Recovery -- A Simple Feature Extraction for High Dimensional Image Representations -- Identifying Feature Relevance Using a Random Forest -- Generalization Bounds for Subspace Selection and Hyperbolic PCA -- Less Biased Measurement of Feature Selection Benefits. |
| Record Nr. | UNISA-996465879603316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Subspace, latent structure and feature selection : Statistical and Optimization Perspectives Workshop, SLSFS 2005, Bohinj, Slovenia, February 23-25, 2005 : revised selected papers / / Craig Saunders ... [et al.] (eds.)
| Subspace, latent structure and feature selection : Statistical and Optimization Perspectives Workshop, SLSFS 2005, Bohinj, Slovenia, February 23-25, 2005 : revised selected papers / / Craig Saunders ... [et al.] (eds.) |
| Edizione | [1st ed. 2006.] |
| Pubbl/distr/stampa | Berlin, : Springer, 2006 |
| Descrizione fisica | 1 online resource (X, 209 p.) |
| Disciplina | 003/.1 |
| Altri autori (Persone) | SaundersCraig |
| Collana |
Lecture notes in computer science
CS sublibrary. SL 1, Theoretical computer science and general issues |
| Soggetto topico |
System identification
Latent structure analysis Computer vision |
| ISBN | 3-540-34138-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Invited Contributions -- Discrete Component Analysis -- Overview and Recent Advances in Partial Least Squares -- Random Projection, Margins, Kernels, and Feature-Selection -- Some Aspects of Latent Structure Analysis -- Feature Selection for Dimensionality Reduction -- Contributed Papers -- Auxiliary Variational Information Maximization for Dimensionality Reduction -- Constructing Visual Models with a Latent Space Approach -- Is Feature Selection Still Necessary? -- Class-Specific Subspace Discriminant Analysis for High-Dimensional Data -- Incorporating Constraints and Prior Knowledge into Factorization Algorithms – An Application to 3D Recovery -- A Simple Feature Extraction for High Dimensional Image Representations -- Identifying Feature Relevance Using a Random Forest -- Generalization Bounds for Subspace Selection and Hyperbolic PCA -- Less Biased Measurement of Feature Selection Benefits. |
| Altri titoli varianti |
Statistical and Optimization Perspectives Workshop
SLSFS 2005 |
| Record Nr. | UNINA-9910483873203321 |
| Berlin, : Springer, 2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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