Statistical learning and data science
"Data analysis is changing fast. Driven by a vast range of application domains and affordable tools, machine learning has become mainstream. Unsupervised data analysis, including cluster analysis, factor analysis, and low dimensionality mapping methods continually being updated, have reached ne...
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Boca Raton, Fla. :
CRC Press
2012.
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| Edición: | 1st edition |
| Colección: | Series in computer science and data analysis.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009627971706719 |
Tabla de Contenidos:
- Front Cover; Contents; Preface; Contributors; I. Statistical and Machine Learning; 1. Mining on Social Networks; 2. Large-Scale Machine Learning with Stochastic Gradient Descent; 3. Fast Optimization Algorithms for Solving SVM+; 4. Conformal Predictors in Semisupervised Case; 5. Some Properties of Infinite VC-Dimension Systems; II. Data Science, Foundations, and Applications; 6. Choriogenesis: the Dynamical Genesis of Space and Its Dimensions, Controlled by Correspondence Analysis; 7. Geometric Data Analysis in a Social Science Research Program: The Case of Bourdieu's Sociology
- 8. Semantics from Narrative: State of the Art and Future Prospects9. Measuring Classifier Performance: On the Incoherence of the Area under the ROC Curve and What to Do about It; 10. A Clustering Approach to Monitor System Working: An Application to Electric Power Production; 11. Introduction to Molecular Phylogeny; 12. Bayesian Analysis of Structural Equation Models Using Parameter Expansion; III. Complex Data; 13. Clustering Trajectories of a Three-Way Longitudinal Dataset; 14. Trees with Soft Nodes: A New Approach to the Construction of Prediction Trees from Data; 15. Synthesis of Objects
- 16. Functional Data Analysis: An Interdisciplinary Statistical Topic17. Methodological Richness of Functional Data Analysis; Bibliography
