SelfOrganizing Maps
 362 Pages
 1995
 0.26 MB
 2957 Downloads
 English
Springer Berlin Heidelberg , Berlin, Heidelberg
Mathematics, Physics, Telecommunic
About the Edition
The SelfOrganizing 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, wellorganized form, accessible without prior expert knowledge. Still the contents are handled with theoretical rigor.
Statement  by Teuvo Kohonen 
Series  Springer Series in Information Sciences  30, Springer Series in Information Sciences  30 
Classifications  

LC Classifications  QH505 
The Physical Object  
Format  [electronic resource] / 
Pagination  1 online resource (xvi, 362p. 94 illus., 1 illus. in color.) 
ID Numbers  
Open Library  OL27086997M 
ISBN 10  3642976123, 3642976107 
ISBN 13  9783642976124, 9783642976100 
OCLC/WorldCa  851381250 
The SelfOrganizing 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 SelfOrganizing 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). SelfOrganizing 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 SelfOrganizing Maps: Edition : Teuvo Kohonen.
Teuvo Kohonen's selforganizing 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 selforganizing map  Proceedings of the IEEE SelfOrganizing Maps book IEEE Created Date: 2/25/ AMFile Size: 1MB. Hämäläinen T Parallel implementation of selforganizing maps SelfOrganizing neural networks, () Lim T, Loh W and Shih Y () A Comparison of Prediction Accuracy, Complexity, and Training Time of ThirtyThree Old and New Classification Algorithms, Machine Language,(), Online publication date: 1Sep 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. SelfOrganizing 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 SelfOrganizing Feature Map. 5 SelfOrganizing Map (cont.) • Provides a topology preserving mapping from the high dimensional space to map units.
Description SelfOrganizing Maps PDF
Map units, or neurons, usually form a twodimensional lattice and thus the SelfOrganizing Maps book is a mapping from high dimensional space onto a Size: KB. An Introduction to SelfOrganizing Maps. J Atlantis Press Book  in x in book_Kahraman.
Chapter An Introduction to SelfOrganizing 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 selforganizing maps for navigation in document collections, including Internet applications. Selforganizing 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 betterknown alternative being ed on: J The growing selforganizing map (GSOM) is a growing variant of the selforganizing map.
Details SelfOrganizing 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 SelfOrganizing 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: SpringerVerlag Berlin Heidelberg. Inroduction.
Download SelfOrganizing Maps FB2
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 Spaceintime and timeinspace selforganizing maps for exploring spatiotemporal patterns Proceedings of the 12th Eurographics / IEEE  VGTC conference on Visualization, ().
A selforganizing map (SOM) is a type of artificial neural network (ANN) that is trained using unsupervised learning to produce a lowdimensional (typically twodimensional), discretized representation of the input space of the training samples, called a map, and is therefore a method to do dimensionality reduction.
Selforganizing maps differ Author: Abhinav Ralhan. Kohonen selforganizing maps (SOM) (Kohonen, ) are feedforward networks that use an unsupervised learning approach through a process called selforganization.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 selforganizing map formation, including recent developments. Selforganizing 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. Selforganizing systems exist in nature, including nonliving as well as living world, they exist in manmade systems, but also in the world of abstract ideas, [12].
SelfOrganizing Map Neural networks of neurons with lateral communication of neurons topologically organized as selforganizing maps are common in Size: 2MB. SelfOrganizing 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; NeuralNetwork Enhanced Visualization of HighDimensional Data; SOMbased Applications in Remote Sensing; Segmentation of Satellite Images Using SOM; Face Recognition.
SelfOrganizing Maps deals with the most popular artificial neuralnetwork algorithm of the unsupervisedlearning category, viz. the SelfOrganizing 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 : SpringerVerlag Berlin Heidelberg.
Thus in this book, we are going to deal only with 0D, 1D, and 2D Kohonen networks. Kohonen SelfOrganizing Maps (SOMs), in addition to the traditional single layer competitive neural networks (in this book, the 0D Kohonen network), add the concept of neighborhood neurons. Product Information.
This book presents analysis and applications of selforganizing maps in various domains. The selforganizing map, first explained by Finnish scientist Teuvo Kohonen, can be used for a broad spectrum of fields and this book explains how the original selforganizing map along with its variants and extensions can be applied in diverse fields.
SelfOrganizing 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 realworld problems.
Selforganizing 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.
Selforganizing maps deals with the most popular artificial neuralnetwork algorithm of the unsupervisedlearning category, the SelfOrganizing 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 selforganizing 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. Selforganizing maps. [Teuvo Kohonen]  The SelfOrganizing 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 SelfOrganizing 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.
SelfOrganising 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. Selforganizing maps go back to the s, and the credit for introducing them goes to Teuvo Kohonen, the man you see in the picture below.
Selforganizing maps are even often referred to as Kohonen maps.1 SelfOrganizing 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 directconCited by:





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