Name: Data Mining Algorithms in C++: Data Patterns and Algorithms for Modern Applications (en Inglés)
Price: 130250.00 CLP
Availability: InStock
Author: Timothy Masters
ISBN: 9781484233146

portada Data Mining Algorithms in C++: Data Patterns and Algorithms for Modern Applications (en Inglés)

Formato

Libro Físico

Editorial

Apress

Autor

Timothy Masters

Año

2017

Idioma

Inglés

N° páginas

286

Encuadernación

Tapa Blanda

ISBN13

9781484233146

N° edición

Categorías

Data mining: minería de datos

Data Mining Algorithms in C++: Data Patterns and Algorithms for Modern Applications (en Inglés)

Timothy Masters (Autor) · Apress · Tapa Blanda

Libro Físico

$ 130.250

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Origen: Reino Unido (Costos de importación incluídos en el precio)

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Reseña del libro "Data Mining Algorithms in C++: Data Patterns and Algorithms for Modern Applications (en Inglés)"

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