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Springer Self-Organizing Maps - Neural Network Algorithm Book

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Description

Explore the fundamentals and advanced applications of the Self-Organizing Map (SOM) with this comprehensive guide from Springer. As the most popular artificial neural network algorithm in the unsupervised learning category, the SOM is a vital tool for solving complex real-world problems across numerous industries. This text provides a deep look into why thousands of research articles have been published on this subject. This edition serves as a massive knowledge base, featuring a survey of over 2000 contemporary studies. It covers the latest results and developments in the field, making it an essential resource for researchers and professionals. Whether you are working in statistics, signal processing, control theory, or financial analyses, this book provides the analytical framework needed for modern data science. It is also highly relevant for specialists in experimental physics, chemistry, and medicine who utilize the SOM as a standard analytical tool.

Key Features

Comprehensive coverage of the Self-Organizing Map (SOM), the leading artificial neural network algorithm for unsupervised learning.

Includes a detailed survey of over 2000 contemporary studies to provide the most recent research results and updates.

Applicable across diverse scientific fields including statistics, signal processing, control theory, and financial analyses.

Provides essential analytical tools for specialized sectors such as experimental physics, chemistry, and medicine.

A foundational resource for understanding how industrial projects use SOM to solve hard real-world problems.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
16 November 2000
Listed Since
15 February 2007

Barcode

No barcode data available

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