| Dewey Class |
006.312 |
| Title |
Data Analysis and Optimization (EBook :) : In Honor of Boris Mirkin's 80th Birthday / / edited by Boris Goldengorin, Sergei Kuznetsov. |
| Added Personal Name |
Goldengorin, Boris |
| Kuznetsov, Sergei |
| Other name(s) |
SpringerLink (Online service) |
| Edition statement |
1st ed. 2023. |
| Publication |
Cham : : Springer Nature Switzerland : : Imprint: Springer, , 2023. |
| Physical Details |
XXXV, 422 p. 1 illus. : online resource. |
| Series |
Springer Optimization and Its Applications 1931-6836 ; ; 202 |
| ISBN |
9783031316548 |
| Summary Note |
This book presents the state-of-the-art in the emerging field of data science and includes models for layered security with applications in the protection of sites—such as large gathering places—through high-stake decision-making tasks. Such tasks include cancer diagnostics, self-driving cars, and others where wrong decisions can possibly have catastrophic consequences. Additionally, this book provides readers with automated methods to analyze patterns and models for various types of data, with applications ranging from scientific discovery to business intelligence and analytics. The book primarily includes exploratory data analysis, pattern mining, clustering, and classification supported by real life case studies. The statistical section of this book explores the impact of data mining and modeling on the predictability assessment of time series. Further new notions of mean values based on ideas of multi-criteria optimization are compared with their conventional definitions, leading to new algorithmic approaches to the calculation of the suggested new means. The style of the written chapters and the provision of a broad yet in-depth overview of data mining, integrating novel concepts from machine learning and statistics, make the book accessible to upper level undergraduate and graduate students in data mining courses. Students and professionals specializing in computer and management science, data mining for high-dimensional data, complex graphs and networks will benefit from the cutting-edge ideas and practically motivated case studies in this book.: |
| Contents note |
Preface and Book of Abstracts -- Chapter. 1. Optimal Layered Defense for Site Protection -- Chapter. 2. SARAH-based Variance-reduced Algorithm for Stochastic Finite-sum Cocoercive Variational Inequalities -- Chapter. 3. Dimensionality reduction using pseudo-Boolean polynomials for cluster analysis -- Chapter. 4. Pseudo-Boolean polynomials approach to edge detection and image segmentation -- Chapter. 5. Purifying Data by Machine Learning with Certainty Levels -- Chapter. 6. On impact of data models on predictability assessment of time series -- Chapter. 7. A three-step method for audience extension in Internet advertising using an industrial taxonomy -- Chapter. 8. From Prebase in Automata Theory to Data Analysis: Boris Mirkin's Way -- Chapter. 9. Manipulability of aggregation procedures for the case of large numbers of voters -- Chapter. 10. Preferences over mixed manna -- Chapter. 11. About Some Clustering Algorithms in Evidence Theory -- Chapter. 12. Inferring Multiple Consensus Trees and Supertrees Using Clustering: A Review -- Chapter. 13. Anomaly Detection With Neural Network Using a Generator -- Chapter. 14. Controllability of triangular systems with phase space change -- Chapter. 15. A Parallel Linear Active Set Method -- Chapter. 16. Mean Values: A Multicriterial Analysis -- Chapter. 17. Data and Text Interpretation in Social Media: Urban Planning Conflicts -- Chapter. 18. Visual Explainable Machine Learning for High-Stake Decision-Making with Worst Case Estimates -- Chapter. 19. Algorithm of trading on the stock market, providing satisfactory results -- Chapter. 20. Classification using Marginalized Maximum Likelihood Estimation and Black-Box Variational Inference -- Chapter. 21. Generating Genomic Maps of Z-DNA with the Transformer Algorithm -- Chapter. 22. Manipulation by Coalitions in Voting with Incomplete Information -- Chapter. 23. Rethinking Probabilistic Topic Modeling from the Point of View of Classical Non-Bayesian Regularization.<p>< p=""></p> . |
| Mode of acces to digital resource |
Digital reproduction.- |
| Cham : |
| Springer International Publishing, |
| 2023. - |
| Mode of access: World Wide Web. System requirements: Internet Explorer 6.0 (or higher) or Firefox 2.0 (or higher). Available as searchable text in PDF format. |
| System details note |
Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users). |
| Internet Site |
https://doi.org/10.1007/978-3-031-31654-8 |
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