Computational, classification, financial, statistical and stochastic methods
著者
書誌事項
Computational, classification, financial, statistical and stochastic methods
（Innovation, entrepreneurship, management series, . Big data,
ISTE , Wiley, 2020
- : hardback
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
Part 1. Computational Data Analysis and Methods 1. Semi-supervised Learning Based on Distributionally Robust Optimization, Jose Blanchet and Yang Kang. 2. Updating of PageRank in Evolving Treegraphs, Benard Abola, Pitos Seleka Biganda, Christopher Engstoerm, John Magero Mango, Godwin Kakuba and Sergei Silvestrov. 3. Exploring The Relationship Between Ordinary PageRank, Lazy PageRank and Random Walk with Backstep PageRank for Different Graph Structures, Pitos Seleka Biganda, Benard Abola, Christopher Engstoerm, John Magero Mango, Godwin Kakuba and Sergei Silvestrov. 4. On the Behavior of Alternative Splitting Criteria for CUB Model-based Trees, Carmela Cappelli, Rosaria Simone and Francesca Di Iorio. 5. Investigation on Life Satisfaction Through (Stratified) Chain Regression Graph Models, Federica Nicolussi and Manuela Cazzaro. Part 2. Classification Data Analysis and Methods 6. Selection of Proximity Measures for a Topological Correspondence Analysis, Rafik Abdelssam. 7. Support Vector Machines: A Review and Applications in Statistical Process Monitoring, Anastasios Apsemidis and Stelios Psarakis. 8. Binary Classification Techniques: An Application on Simulated and Real Bio-medical Data, Fragkiskos G. Bersimis, Iraklis Varlamis, Malvina Vamvakari and Demosthenes B. Panagiotakos. 9. Some Properties of the Multivariate Generalized Hyperbolic Models, Stergios B. Fotopoulos, Venkata K. Jandhyala and Alex Paparas. 10. On Determining the Value of Online Customer Satisfaction Ratings A Case-based Appraisal, Jim Freeman. 11. Projection Clustering Unfolding: A New Algorithm for Clustering Individuals or Items in a Preference Matrix, Mariangela Sciandra, Antonio D Ambrosio and Antonella Plaia.
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