£113.76

Springer - Data Complexity in Pattern Recognition Book

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Description

Explore the fundamental role of data complexity in modern computing with this advanced text from Springer. As automatic pattern recognition becomes essential across science, engineering, social sciences, and finance, understanding the underlying data structures is more important than ever. This book examines how data complexity shapes the theories and techniques used in various disciplines. It provides a deep look into current classification techniques, analyzing both their strengths and their deficiencies. By studying the algorithms that drive these processes, readers gain a clearer understanding of how to navigate complex datasets. Designed for those working in AI and machine learning, this resource offers practical guidance on selecting the right pattern recognition classification techniques. It also helps professionals and students set realistic expectations for classification performance, making it a valuable addition to your technical library.

Key Features

Examines how data complexity influences theory and techniques across multiple scientific and engineering disciplines.

Analyzes the strengths and deficiencies of current classification techniques used in modern computing.

Provides detailed information on the algorithms that drive automatic pattern recognition processes.

Offers practical guidance for choosing the most effective pattern recognition classification techniques for specific tasks.

Helps readers establish realistic expectations regarding classification performance in various applications.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
22 October 2010
Listed Since
01 October 2010

Barcode

No barcode data available

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