The advent of computerization has improved our capabilities in terms of generating and collecting data from myriad of sources to a large extent. A huge amount of data has inundated nearly in all walks of lives. Such growth in data has led to an immediate need for the development of new tools, which can be of help to us in an
intelligent manner. In the light of all these developments, this book dwells on neural learning methods and it aims at shedding light on those applications where sample data are available but algorithms for analysis are missing.
Author(s): Yeliz Karaca, Carlo Cattani
Publisher: De Gruyter
Year: 2019
Language: English
Pages: 398
Tags: Data Analysis: Computational Methods
Cover......Page 1
Computational
Methods for Data
Analysis
......Page 5
© 2019......Page 6
Preface......Page 7
Acknowledgment......Page 9
Contents
......Page 11
1 Introduction......Page 15
2 Dataset......Page 23
3 Data preprocessing and model evaluation......Page 85
4 Algorithms......Page 117
5 Linear model and multilinear model......Page 161
6 Decision Tree......Page 187
7 Naive Bayesian classifier......Page 243
8 Support vector machines algorithms......Page 265
9 k-Nearest neighbor algorithm......Page 287
10 Artificial neural networks algorithm......Page 303
11 Fractal and multifractal methods with ANN......Page 337
Index......Page 389
