Computational Methods for Data Analysis / Carlo Cattani, Yeliz Karaca.
By: Karaca, Yeliz [author.]
.
Contributor(s): Cattani, Carlo [author.].
Material type: 
BookSeries: De Gruyter Textbook.Publisher: Berlin ; Boston : De Gruyter, [2018]Copyright date: ©2019Description: 1 online resource (XII, 383 p.).Content type: text Media type: computer Carrier type: online resourceISBN: 9783110496369.Subject(s): Mathematical statistics | Probabilities | Statistics -- Data processing | Bioinformatik | Datenanalyse | Maschinelles Lernen | Massendaten | Ökonometrie | MATHEMATICS / AppliedAdditional physical formats: No title; No titleOther classification: SK 990 Online resources: Click here to access online | Click here to access online | Cover 
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
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http://purl.org/coar/access_right/c_16ec
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.
Mode of access: Internet via World Wide Web.
In English.
Description based on online resource; title from PDF title page (publisher's Web site, viewed 30. Aug 2021)
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