Название: Computational Methods for Data Analysis
Автор: Yeliz Karaca, Carlo Cattani
Издательство: de Gruyter
Год: 2018
Страниц: 398
Язык: английский
Формат: True PDF
Размер: 10.1 MB

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.

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. Successful applications of artificial intelligence have already been extensively introduced into our lives. There already exist some commercial software developed for the recognition of handwriting and speech.

In addition, we provide simple and to-the-point explanations so that it will be possible for our readers to make conversions from mathematical operations into programme dimension. The applications in the book cover three different datasets relating to the algorithms in each section.








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