Computational Methods for Data Analysis / / Carlo Cattani, Yeliz Karaca.

• Superior document: Title is part of eBook package: De Gruyter DG Plus eBook-Package 2019
• VerfasserIn: Karaca, Yeliz,; Cattani, Carlo,
• Place / Publishing House: Berlin ;, Boston : : De Gruyter, , [2018]; ©2019
• Year of Publication: 2018
• Language: English
• Series: De Gruyter Textbook
• Physical Description: 1 online resource (XII, 383 p.)
• Other title: Frontmatter --; Preface --; Acknowledgment --; Contents --; 1. Introduction --; 2. Dataset --; 3. Data preprocessing and model evaluation --; 4. Algorithms --; 5. Linear model and multilinear model --; 6. Decision Tree --; 7. Naive Bayesian classifier --; 8. Support vector machines algorithms --; 9. k-Nearest neighbor algorithm --; 10. Artificial neural networks algorithm --; 11. Fractal and multifractal methods with ANN --; Index
• Format: Mode of access: Internet via World Wide Web.
• ISBN: 9783110496369; 9783110719567; 9783110616859; 9783110604252; 9783110603255; 9783110604023; 9783110603118
• DOI: 10.1515/9783110496369
• Access: restricted access
• Hierarchical level: Monograph
• Statement of Responsibility: Carlo Cattani, Yeliz Karaca.

This graduate text covers a variety of mathematical and statistical tools for the analysis of big data coming from biology, medicine and economics. Neural networks, Markov chains, tools from statistical physics and wavelet analysis are used to develop efficient computational algorithms, which are then used for the processing of real-life data using Matlab.