# Self-Organizing Maps

• 362 Pages
• 0.26 MB
• English
by
Springer Berlin Heidelberg , Berlin, Heidelberg
Mathematics, Physics, Telecommunic

The Self-Organizing Map (SOM) algorithm was introduced by the author in 1981. Its theory and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technolgies have already been based on it. The most important practical applications are in exploratory data analysis, pattern recognition, speech analysis, robotics, industrial and medical diagnostics, instrumentation, and control, and literally hundreds of other tasks. In this monograph the mathematical preliminaries, background, basic ideas, and implications are expounded in a clear, well-organized form, accessible without prior expert knowledge. Still the contents are handled with theoretical rigor.

Classifications The Physical Object Statement by Teuvo Kohonen Series Springer Series in Information Sciences -- 30, Springer Series in Information Sciences -- 30 LC Classifications QH505 Format [electronic resource] / Pagination 1 online resource (xvi, 362p. 94 illus., 1 illus. in color.) Open Library OL27086997M ISBN 10 3642976123, 3642976107 ISBN 13 9783642976124, 9783642976100 OCLC/WorldCa 851381250

The Self-Organizing Map (SOM), with its variants, is the most popular artificial neural network algorithm in the unsupervised learning category. About research articles on it have appeared in the open literature, and many industrial projects use the SOM as a tool for solving hard real world problems.

Many fields of science have adopted the Cited by: Since the second edition of this book came out in earlythe number of scientific papers published on the Self-Organizing Map (SOM) has increased from about to some Also, two special workshops dedicated to the SOM have been organized, not to mention numerous SOM sessions in neural network conferences.

In view of this growing interest it was felt desirable to make extensive 4/5(5). Self-Organizing Maps: Edition 3 - Ebook written by Teuvo Kohonen. Read this book using Google Play Books app on your PC, android, iOS devices. Download for offline reading, highlight, bookmark or take notes while you read Self-Organizing Maps: Edition : Teuvo Kohonen.

Teuvo Kohonen's self-organizing maps (SOM) have been somewhat of a mystery to me. I was unsure how to apply the technology to a financial application I was authoring. From what I've read so far, the mystery is slowly unraveling. Thank you Misters Deboeck and Kohonen.

Title: The self-organizing map - Proceedings of the IEEE Self-Organizing Maps book IEEE Created Date: 2/25/ AMFile Size: 1MB. Hämäläinen T Parallel implementation of self-organizing maps Self-Organizing neural networks, () Lim T, Loh W and Shih Y () A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms, Machine Language,(), Online publication date: 1-Sep 1.

We began by defining what we mean by a Self Organizing Map (SOM) and by a topographic map. We then looked at how to set up a SOM and at the components of self organisation: competition, cooperation, and adaptation. We saw that the self organization has two. Self-Organizing Maps book. Read reviews from world’s largest community for readers.

Since the second edition of this book came out in earlythe num /5. SOFM, the Self-Organizing Feature Map. 5 Self-Organizing Map (cont.) • Provides a topology preserving mapping from the high dimensional space to map units.

### Description Self-Organizing Maps PDF

Map units, or neurons, usually form a two-dimensional lattice and thus the Self-Organizing Maps book is a mapping from high dimensional space onto a Size: KB. An Introduction to Self-Organizing Maps. J Atlantis Press Book - in x in book_Kahraman.

Chapter An Introduction to Self-Organizing Maps. Umut Asan and Secil Ercan. The chapter presents several applications of Kohonen maps for organizing business information—namely, for analysis of Russian banks, industrial companies, and the stock market.

The chapter explains how to use self-organizing maps for navigation in document collections, including Internet applications. Self-organizing maps A SOM is a technique to generate topological representations of data in reduced dimensions.

It is one of a number of techniques with such applications, with a better-known alternative being ed on: J The growing self-organizing map (GSOM) is a growing variant of the self-organizing map.

### Details Self-Organizing Maps FB2

The GSOM was developed to address the issue of identifying a suitable map size in the SOM. It starts with a minimal number of nodes (usually four) and grows new nodes on the boundary based on a heuristic.

About this book The Self-Organizing Map (SOM), with its variants, is the most popular artificial neural network algorithm in the unsupervised learning category.

About research articles on it have appeared in the open literature, and many industrial projects use the SOM as a Brand: Springer-Verlag Berlin Heidelberg. Inroduction.

Self Organizing Maps (SOMs) are a tool for visualizing patterns in high dimensional data by producing a 2 dimensional representation, which (hopefully) displays meaningful patterns in the higher dimensional structure. SOMs are “trained” with the given data (or a sample of your data) in the following way: The size of map grid is defined.

Andrienko G, Andrienko N, Bremm S, Schreck T, von Landesberger T, Bak P and Keim D Space-in-time and time-in-space self-organizing maps for exploring spatiotemporal patterns Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization, ().

A self-organizing map (SOM) is a type of artificial neural network (ANN) that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional), discretized representation of the input space of the training samples, called a map, and is therefore a method to do dimensionality reduction.

Self-organizing maps differ Author: Abhinav Ralhan. Kohonen self-organizing maps (SOM) (Kohonen, ) are feed-forward networks that use an unsupervised learning approach through a process called self-organization.A Kohonen network consists of two layers of processing units called an input layer and an output layer.

There are no hidden units. When an input pattern is fed to the network, the units in the output layer compete with each other. This book provides an overview of self-organizing map formation, including recent developments. Self-organizing maps form a branch of unsupervised learning, which is the study of what can be determined about the statistical properties of input data without explicit feedback from a teacher.

The articles are drawn from the journal Neural book consists of five sections. Self-organizing systems exist in nature, including non-living as well as living world, they exist in man-made systems, but also in the world of abstract ideas, [12].

Self-Organizing Map Neural networks of neurons with lateral communication of neurons topologically organized as self-organizing maps are common in Size: 2MB. Self-Organizing Maps by George K Matsopoulos. Publisher: InTech ISBN Number of pages: Description: From the table of contents: Learning the Number of Clusters in Self Organizing Map; Neural-Network Enhanced Visualization of High-Dimensional Data; SOM-based Applications in Remote Sensing; Segmentation of Satellite Images Using SOM; Face Recognition.

Self-Organizing Maps deals with the most popular artificial neural-network algorithm of the unsupervised-learning category, viz. the Self-Organizing Map (SOM).As this book is the main monograph on the subject, it discusses all the relevant aspects ranging from the history, motivation, fundamentals, theory, variants, advances, and applications, to the hardware of : Springer-Verlag Berlin Heidelberg.

Thus in this book, we are going to deal only with 0-D, 1-D, and 2-D Kohonen networks. Kohonen Self-Organizing Maps (SOMs), in addition to the traditional single layer competitive neural networks (in this book, the 0-D Kohonen network), add the concept of neighborhood neurons. Product Information.

This book presents analysis and applications of self-organizing maps in various domains. The self-organizing map, first explained by Finnish scientist Teuvo Kohonen, can be used for a broad spectrum of fields and this book explains how the original self-organizing map along with its variants and extensions can be applied in diverse fields.

Self-Organizing Map (SOM), with its variants, is the most popular artificial neural network algorithm in the unsupervised learning category. About research articles on it have appeared in the open literature, and many industrial projects use the SOM as a tool for solving hard real-world problems.

Self-organizing maps are one very fun concept and very different from the rest of the neural network world. They use the unsupervised learning to create a map or a mask for the input data. They provide an elegant solution for large or difficult to interpret data sets.

Self-organizing maps deals with the most popular artificial neural-network algorithm of the unsupervised-learning category, the Self-Organizing Map (SOM). As this book is the main monograph on the subject, it discusses all the relevant aspects rangin from the history, motivation, fundamentals, theory, variants, advances, and applications, to.

Kohonen self-organizing maps [4] are a very effective means of coordinated online clustering, the use of which permits to solve the task in online mode and the evolutionary algorithms that permit.

Get this from a library. Self-organizing maps. [Teuvo Kohonen] -- The Self-Organizing Map (SOM) algorithm was introduced by the author in Its theory and many applications form one of the major approaches to the contemporary artificial neural networks field.

The algorithm is an implementation of the basic Self-Organizing Map algorithm based on the description in Chapter 3 of the seminal book on the technique [Kohonen].

The implementation is configured with a $4 \times 5$ grid of nodes, the Euclidean distance measure is used to determine the BMU and neighbors, a Bubble neighborhood function is used.

Self-Organising Maps: Applications in GI Science brings together the latest geographical research where extensive use has been made of the SOM algorithm, and provides readers with a snapshot of these tools that can then be adapted and used in new research projects.

The book begins with an overview of the SOM technique and the most commonly used (and freely available) software; it is then. SOMs will be our first step into the unsupervised category. Self-organizing maps go back to the s, and the credit for introducing them goes to Teuvo Kohonen, the man you see in the picture below.

Self-organizing maps are even often referred to as Kohonen maps.1 Self-Organizing Maps 3 We then discuss the applications of the SOM algorithm, for which thousands of them have been reported in the open literature. Rather than attempting for an extensive overview, we group the applications into three areas: vector quantization, latteris the most important onesince it is a directcon-Cited by: