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Computational Methods for Data Analysis

โœ Scribed by Yeliz Karaca; Carlo Cattani

Publisher
De Gruyter
Year
2018
Tongue
English
Leaves
396
Category
Library
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โœฆ Synopsis

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.

  • Introduces mathematical and statistical methods for data analysis, with applications to biology, medicine and economics
  • Combines theory and applications in the processing of real-life data
  • Algorithms and examples using Matlab are included

โœฆ Table of Contents

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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