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Springer Advances in Self-Organizing Maps and Machine Learning

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Description

Explore the latest developments in unsupervised machine learning with this comprehensive collection from Springer. This volume contains the proceedings from the 13th International Workshop on Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization (WSOM+), held in Barcelona, Spain. Since its inception in 1997, this conference has been a primary source for state of the art research in unsupervised learning. This specific installment in the Intelligent Systems and Computing series focuses on the widely used self-organizing map (SOM) method. Beyond SOM, the text covers essential topics including learning vector quantization, advanced clustering techniques, and data visualization methods. Readers will find theoretical research and practical insights that address the evolving landscape of AI and machine learning. This book serves as a vital resource for researchers, students, and professionals looking to understand modern approaches to pattern recognition and data structure analysis through unsupervised methods.

Key Features

Contains research papers from the 13th International Workshop on Self-Organizing Maps (WSOM+) held in Barcelona.

Covers essential unsupervised machine learning topics including Learning Vector Quantization and clustering.

Part of the recognized Intelligent Systems and Computing series (AISC) by Springer.

Provides deep insights into the self-organizing map (SOM) method and its many applications.

Includes specialized research on data visualization techniques for complex datasets.

Offers theoretical research updates for professionals in the field of AI and machine learning.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
28 April 2019
Listed Since
04 April 2019

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