“…In this book we investigate how structured Web data can be leveraged for ranking with the goal to improve the effectiveness of search. …”

Tabla de Contenidos: “…Platts ; revised and enlarged by G. S. A. Ranking -- Part. 2. Syntax / by George S. A. Ranking…”

Table of contents only
Contributor biographical information
Publisher description

Acceso restringido con credenciales UPSA

Materias: “…Rank, Otto…”

Materias: “…Ranke, Leopold von Estudios, ensayos, etc…”

Materias: “…Ranke, Leopold von Historia Filosofía…”

Tabla de Contenidos: “…Contiene: Directorio de empresas 2008 ; Ranking de empresas 2007 de Murcia, Navarra, País Vasco, Santander y Cantabria…”

Tabla de Contenidos: “…Front Cover -- Deep Learning Through Sparse and Low-Rank Modeling -- Copyright -- Contents -- Contributors -- About the Editors -- Preface -- Acknowledgments -- 1 Introduction -- 1.1 Basics of Deep Learning -- 1.2 Basics of Sparsity and Low-Rankness -- 1.3 Connecting Deep Learning to Sparsity and Low-Rankness -- 1.4 Organization -- References -- 2 Bi-Level Sparse Coding: A Hyperspectral Image Classi cation Example -- 2.1 Introduction -- 2.2 Formulation and Algorithm -- 2.2.1 Notations -- 2.2.2 Joint Feature Extraction and Classi cation -- 2.2.2.1 Sparse Coding for Feature Extraction -- 2.2.2.2 Task-Driven Functions for Classi cation -- 2.2.2.3 Spatial Laplacian Regularization -- 2.2.3 Bi-level Optimization Formulation -- 2.2.4 Algorithm -- 2.2.4.1 Stochastic Gradient Descent -- 2.2.4.2 Sparse Reconstruction -- 2.3 Experiments -- 2.3.1 Classi cation Performance on AVIRIS Indiana Pines Data -- 2.3.2 Classi cation Performance on AVIRIS Salinas Data -- 2.3.3 Classi cation Performance on University of Pavia Data -- 2.4 Conclusion -- 2.5 Appendix -- References -- 3 Deep l0 Encoders: A Model Unfolding Example -- 3.1 Introduction -- 3.2 Related Work -- 3.2.1 l0- and l1-Based Sparse Approximations -- 3.2.2 Network Implementation of l1-Approximation -- 3.3 Deep l0 Encoders -- 3.3.1 Deep l0-Regularized Encoder -- 3.3.2 Deep M-Sparse l0 Encoder -- 3.3.3 Theoretical Properties -- 3.4 Task-Driven Optimization -- 3.5 Experiment -- 3.5.1 Implementation -- 3.5.2 Simulation on l0 Sparse Approximation -- 3.5.3 Applications on Classi cation -- 3.5.4 Applications on Clustering -- 3.6 Conclusions and Discussions on Theoretical Properties -- References -- 4 Single Image Super-Resolution: From Sparse Coding to Deep Learning -- 4.1 Robust Single Image Super-Resolution via Deep Networks with Sparse Prior -- 4.1.1 Introduction -- 4.1.2 Related Work…”