Karaca, Yeliz,  <br/><br/>
Computational Methods for Data Analysis / 
Carlo Cattani, Yeliz Karaca. 
 - 1 online resource (XII, 383 p.) 
 - De Gruyter Textbook .
<br/><br/> 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 
<br/><br/> restricted access  http://purl.org/coar/access_right/c_16ec 
<br/><br/> 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. 
<br/><br/><br/>Mode of access: Internet via World Wide Web. 
<br/><br/><br/>In English. 
<br/><br/><label>ISBN: </label>9783110496369 
<br/><br/><label>Standard No.: </label>10.1515/9783110496369  doi 
<br/><br/><label>LCCN: </label>2018951307 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Mathematical statistics.<br/>Probabilities.<br/>Statistics--Data processing.<br/>Bioinformatik.<br/>Datenanalyse.<br/>Maschinelles Lernen.<br/>Massendaten.<br/>Ökonometrie.<br/>MATHEMATICS / Applied.
<br/><br/><label>LC Class. No.: </label>QA276.4  / .K355 2019