| Description |
1 online resource |
| Series |
Big data, artificial intelligence and data analysis set ; volume 5 |
|
Innovation, entrepreneurship and management series. Big data, artificial intelligence and data analysis set ; v. 5
|
| Contents |
Cover -- Half-Title Page -- Title Page -- Copyright Page -- Contents -- Preface -- PART 1: Computational Data Analysis and Methods -- 1. Semi-supervised Learning Based on Distributionally Robust Optimization -- 1.1. Introduction -- 1.2. Alternative semi-supervised learning procedures -- 1.3. Semi-supervised learning based on DRO -- 1.3.1. Defining the optimal transport discrepancy -- 1.3.2. Solving the SSL-DRO formulation -- 1.4. Error improvement of our SSL-DRO formulation -- 1.5. Numerical experiments -- 1.6. Discussion on the size of the uncertainty set -- 1.7. Conclusion |
|
1.8. Appendix: supplementary material: technical details for theorem 1.1 -- 1.8.1. Assumptions of theorem 1.1 -- 1.8.2. Revisit theorem 1.1 -- 1.8.3. Proof of theorem 1.1 -- 1.9. References -- 2. Updating of PageRank in Evolving Treegraphs -- 2.1. Introduction -- 2.2. Abbreviations and definitions -- 2.3. Finding components -- 2.3.1. Isolation of vertices in the graph -- 2.3.2. Keeping track of every vertex in the components -- 2.4. Maintaining the level of cycles -- 2.5. Calculating PageRank -- 2.6. PageRank of a tree with at least a cycle after addition of an edge |
|
5. Investigation on Life Satisfaction Through (Stratified) Chain Regression Graph Models -- 5.1. Introduction -- 5.2. Methodology -- 5.3. Application -- 5.3.1. Survey on multiple aims analysis -- 5.4. Conclusion -- 5.5. References -- PART 2: Classification Data Analysis and Methods -- 6. Selection of Proximity Measures for a Topological Correspondence Analysis -- 6.1. Introduction -- 6.2. Topological correspondence -- 6.2.1. Comparison and selection of proximity measures -- 6.2.2. Statistical comparisons between two proximity measures -- 6.3. Application to real data and empirical results |
| Summary |
Data analysis as an area of importance has grown exponentially, especially during the past couple of decades. This can be attributed to a rapidly growing computer industry and the wide applicability of computational techniques, in conjunction with new advances of analytic tools. This being the case, the need for literature that addresses this is self-evident. New publications are appearing, covering the need for information from all fields of science and engineering, thanks to the universal relevance of data analysis and statistics packages. This book is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians who have been working at the forefront of data analysis. The chapters included in this volume represent a cross-section of current concerns and research interests in these scientific areas. The material is divided into two parts: Computational Data Analysis, and Classification Data Analysis, with methods for both - providing the reader with both theoretical and applied information on data analysis methods, models and techniques and appropriate applications |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from PDF title page (EBSCO, viewed November 25, 2020) |
| Subject |
Quantitative research.
|
|
Data mining.
|
|
Data Mining
|
|
Data mining
|
|
Quantitative research
|
| Form |
Electronic book
|
| Author |
Makrides, Andreas (Lecturer in Mathematics and Statistics), editor
|
|
Karagrigoriou, Alex, editor
|
|
Skiadas, Christos H., editor
|
| ISBN |
9781119721864 |
|
1119721865 |
|
9781119721826 |
|
1119721822 |
|
9781119721871 |
|
1119721873 |
|
