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| Link zu diesem Datensatz | https://d-nb.info/1386530093 |
| Titel | Kernel Methods for Omics Data Mining : Theory and Applications / by Hao Jiang, Wai-Ki Ching |
| Person(en) |
Jiang, Hao (Verfasser) Ching, Wai-Ki (Verfasser) |
| Organisation(en) | SpringerLink (Online service) (Sonstige) |
| Ausgabe | 1st ed. 2026 |
| Verlag | Singapore : Springer Nature Singapore, Imprint: Springer |
| Zeitliche Einordnung | Erscheinungsdatum: 2026 |
| Umfang/Format | Online-Ressource, X, 232 p. 90 illus., 89 illus. in color. : online resource. |
| Andere Ausgabe(n) |
Printed edition:: ISBN: 978-981-95-3128-8 Printed edition:: ISBN: 978-981-95-3130-1 Printed edition:: ISBN: 978-981-95-3131-8 |
| Inhalt | Omics Data: Acquisition and Mining -- Omics Data: Acquisition and Mining -- Kernels and Spectrum Perturbations -- Hadamard Kernel SVM with Applications -- Regularized Multiple Kernel Learning Framework -- Correlation Kernels for SVM Classification -- Weighted GTS Kernel and Applications in Drug Side-effect Profiles Prediction -- Single Cell RNA-sequencing Data Analysis -- Kernel Non-negative Matrix Factorization Framework for Single Cell Clustering -- Deep Neural Network with Kernel Nonnegative Matrix Factorization for Single Cell Clustering -- Multi-omics Single-cell Data Integration via High-order Kernel Spectral Clustering |
| Persistent Identifier |
URN: urn:nbn:de:101:1-2601130328038.056647982050 DOI: 10.1007/978-981-95-3129-5 |
| URL | https://doi.org/10.1007/978-981-95-3129-5 |
| ISBN/Einband/Preis | 978-981-95-3129-5 |
| Sprache(n) | Englisch (eng) |
| Beziehungen | Intelligent Control and Learning Systems ; 10 |
| DDC-Notation | 006.31 (maschinell ermittelte DDC-Kurznotation) |
| Sachgruppe(n) | 004 Informatik |
| Online-Zugriff | Archivobjekt öffnen |
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