Data Analysis and Optimization
In Honor of Boris Mirkin's 80th Birthday
Series: Springer Optimization and Its Applications; 202;
- Publisher's listprice EUR 213.99
-
90 774 Ft (86 451 Ft + 5% VAT)
The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.
- Discount 8% (cc. 7 262 Ft off)
- Discounted price 83 512 Ft (79 535 Ft + 5% VAT)
90 774 Ft
Availability
Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
Not in stock at Prospero.
Why don't you give exact delivery time?
Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Edition number 2023
- Publisher Springer
- Date of Publication 24 September 2023
- Number of Volumes 1 pieces, Book
- ISBN 9783031316531
- Binding Hardback
- No. of pages422 pages
- Size 235x155 mm
- Weight 852 g
- Language English
- Illustrations 1 Illustrations, black & white 548
Categories
Short description:
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.
MoreLong description:
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.
MoreTable of Contents:
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 thePoint of View of Classical Non-Bayesian Regularization.
