Data Mining Algorithms in C++: Data Patterns and Algorithms for Modern Applications This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. .

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Detalles bibliogr�ficos

T�tulo

Data Mining Algorithms in C++: Data Patterns and Algorithms for Modern Applications

Editor

Apress

Fecha de publicaci�n

2017

Idioma

Ingl�s

ISBN 10

148423314X

ISBN 13

9781484233146

Encuadernaci�n

paperback

Estado

Very Good

Cat�logos de vendedores

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Sinopsis

Discover hidden relationships among the variables in your data, and learn how to exploit these relationships. This book presents a collection of data-mining algorithms that are effective in a wide variety of prediction and classification applications. All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code.

Many of these techniques are recent developments, still not in widespread use. Others are standard algorithms given a fresh look. In every case, the focus is on practical applicability, with all code written in such a way that it can easily be included into any program. The Windows-based DATAMINE program lets you experiment with the techniques before incorporating them into your own work.

What You'll Learn

Use Monte-Carlo permutation tests to provide statistically sound assessments of relationships present in your data
Discover how combinatorially symmetric cross validation reveals whether your model has true power or has just learned noise by overfitting the data
Work with feature weighting as regularized energy-based learning to rank variables according to their predictive power when there is too little data for traditional methods
See how the eigenstructure of a dataset enables clustering of variables into groups that exist only within meaningful subspaces of the data
Plot regions of the variable space where there is disagreement between marginal and actual densities, or where contribution to mutual information is high

Who This Book Is For

Anyone interested in discovering and exploiting relationships among variables. Although all code examples are written in C++, the algorithms are described in sufficient detail that they can easily be programmed in any language.

Acerca del autor

Timothy Masters has a PhD in statistics and is an experienced programmer. His dissertation was in image analysis. His career moved in the direction of signal processing, and for the last 25 years he's been involved in the development of automated trading systems in various financial markets.

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