Spectral Feature Selection for Data Mining (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) - Image 1

£125.00

CRC Press Spectral Feature Selection for Data Mining (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Name: Spectral Feature Selection for Data Mining (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
Brand: CRC Press
Price: 125.0 GBP
Availability: InStock

Data Mining

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Description

Spectral Feature Selection for Data Mining introduces a novel feature selection technique that establishes a general platform for studying existing feature selection algorithms and developing new algorithms for emerging problems in real-world applications. This technique represents a unified framework for supervised, unsupervised, and semisupervised feature selection. The book explores the latest research achievements, sheds light on new research directions, and stimulates readers to make the next creative breakthroughs. It presents the intrinsic ideas behind spectral feature selection, its theoretical foundations, its connections to other algorithms, and its use in handling both large-scale data sets and small sample problems. The authors also cover feature selection and feature extraction, including basic concepts, popular existing algorithms, and applications. A timely introduction to spectral feature selection, this book illustrates the potential of this powerful dimensionality reduction technique in high-dimensional data processing. Readers learn how to use spectral feature selection to solve challenging problems in real-life applications and discover how general feature selection and extraction are connected to spectral feature selection.

Product Specifications

Brand: CRC Press
Format: hardcover
ASIN: 1439862095
Category: Books > Subjects > Computing & Internet > Databases > Data Storage & Management > Data Mining
Domain: Amazon UK
Release Date: 14 December 2011
Listed Since: 11 October 2010

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CRC Press Spectral Feature Selection for Data Mining (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

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