By Topic

Introduction

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31.0 $31.0
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)

Automation requires computational systems that exhibit some degree of intelligence, in terms of the ability of a system to formulate its own models of the data in question with little or no user intervention. This introductory chapter provides an overview of the content discussed in the subsequent chapters of the book. The book primarily introduces a new approach to the general problem of unsupervised learning, based on the principles of dynamic self-organization. It gives an extensive review of the general problems of unsupervised clustering, with emphasis placed on the inherent relationship that exists between unsupervised learning and self-organization. The book presents self-organizing tree map (SOTM) and its recently successful application in multimedia processing. It describes the developments of the self-organizing hierarchical variance map (SOHVM) and its application in the unsupervised segmentation and visualization of microbiological image data.