Set includes:Analog Training Board ATB - evaluation kit for analog...
Pin header goldpin black 2x10 pins. Straight connector for SMD assembly....
Expansion board with GSM/GPRS module designed for Arduino and Raspberry...
External enclosure designed to protect, transfer and store data on M.2...
External case designed to protect, transfer and store data on M.2 SSD...
Samsung 980 SSD 250 GB. It is equipped with NVMe PCIe 3.0 x4 interface...
Author: Stanisław Osowski
Format: PDF, B5, 388 pages
BTC publishing house
Legionowo - paper edition 2013
This manual presents a synthetic approach to modern methods of data mining. In particular, optimization methods have been presented, including global optimization, linear and logistic regression methods, various classifier solutions, including Bayesian classifiers, decision trees, MLP, RBF and SVM neural networks, expert systems in the form of classifiers and predictors, transformations and reduction methods data dimension, methods for the selection of significant diagnostic features, data grouping and visualization methods, as well as the basic concepts of associative analysis between data.
Examples of the application of data mining methods in medicine are also presented.
The theoretical considerations are supported by examples of specific data mining calculations, implemented in the form of appropriate programs in Matlab.
The book is a unique source of knowledge for undergraduate and graduate students. It can be recommended as a basic extension material for lectures on artificial intelligence, pattern recognition or biomedical engineering in the fields of Computer Science or Automation. It can be used by employees of companies, so-called data analysts, specializing in professional life in data analysis and knowledge discovery from databases and data warehouses.
Review posted in the NetWorld magazine No. 07-08 / 2013
The handbook captures selected issues of data mining. The reader will find in it a clear introduction to the subject, an explanation of the basic concepts and techniques used in this area of knowledge. He will learn many methods and algorithms of optimization, from linear regression to artificial neural networks. You will learn how to classify numerical data, how to solve prediction problems and what methods to use to assess them, and how to solve the problem of acquiring associations between data. It will find a description of the task of data clustering, and more specifically their grouping with the use of unbalanced learning techniques. Get information on data visualization tools, with detailed discussion of graphical capabilities of the universal Matlab software package. Two practical examples of using data mining methods serve to reinforce knowledge. The publication is a universal source of knowledge regarding data mining, which can be used by both students and data analysts who deal with the discovery of knowledge from databases and data warehouses.