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Feature Selection for High-dimensional Data (Hardcover) (Veru00f3nica Bolu00f3n-canedo & Noelia

Feature Selection for High-dimensional Data (Hardcover) (Veru00f3nica Bolu00f3n-canedo & Noelia - image 1 of 1

About this item

This book offers a coherent and comprehensive approach to feature subset selection in the scope of classification problems, explaining the foundations, real application problems and the challenges of feature selection for high-dimensional data.

The authors first focus on the analysis and synthesis of feature selection algorithms, presenting a comprehensive review of basic concepts and experimental results of the most well-known algorithms.

They then address different real scenarios with high-dimensional data, showing the use of feature selection algorithms in different contexts with different requirements and information: microarray data, intrusion detection, tear film lipid layer classification and cost-based features. The book then delves into the scenario of big dimension, paying attention to important problems under high-dimensional spaces, such as scalability, distributed processing and real-time processing, scenarios that open up new and interesting challenges for researchers.

The book is useful for practitioners, researchers and graduate students in the areas of machine learning and data mining.

Number of Pages: 148
Genre: Computers + Internet
Sub-Genre: Data Modeling + Design, Database Management / Data Mining, Artificial Intelligence
Series Title: Artificial Intelligence: Foundations, Theory, and Algorithms
Format: Hardcover
Publisher: Springer Verlag
Author: Veru00f3nica Bolu00f3n-canedo & Noelia Su00e1nchez-marou00f1o & Amparo Alonso-betanzos
Language: English
Street Date: October 14, 2015
TCIN: 50463641
UPC: 9783319218571
Item Number (DPCI): 248-07-4120
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$85.49
MSRPwas $89.99
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