Showing papers by "Teuvo Kohonen published in 1996"

Engineering applications of the self-organizing map

Abstract: The self-organizing map (SOM) method is a new, powerful software tool for the visualization of high-dimensional data. It converts complex, nonlinear statistical relationships between high-dimensional data into simple geometric relationships on a low-dimensional display. As it thereby compresses information while preserving the most important topological and metric relationships of the primary data elements on the display, it may also be thought to produce some kind of abstractions. The term self-organizing map signifies a class of mappings defined by error-theoretic considerations. In practice they result in certain unsupervised, competitive learning processes, computed by simple-looking SOM algorithms. Many industries have found the SOM-based software tools useful. The most important property of the SOM, orderliness of the input-output mapping, can be utilized for many tasks: reduction of the amount of training data, speeding up learning nonlinear interpolation and extrapolation, generalization, and effective compression of information for its transmission.

SOM_PAK: The Self-Organizing Map Program Package

Abstract: The Self-Organizing Map (SOM) represents the result of a vector quantization algorithm that places a number of reference or code-book vectors into a high-dimensional input data space to approximate to its data sets in an ordered fashion. The SOM PAK program package contains all programs necessary for the correct application of the Self-Organizing Map algorithm in the visualization of complex experimental data. The rst version 1.0 of this program package was published in 1992 and since then the package has been updated regularly to include latest improvements in the SOM implementations. This report that contains the last documentation was prepared for bibliographical purposes.

LVQ_PAK: The Learning Vector Quantization Program Package

Abstract: Learning Vector Quantization (LVQ) is a group of algorithms applicable to statistical pattern recognition, in which the classes are described by a relatively small number of codebook vectors, properly placed within each zone such that the decision borders are approximated by the nearest-neighbor rule. The LVQ PAK program package contains all programs necessary for the correct application of certain Learning Vector Quantization algorithms in an arbitrary statistical classiication or pattern recognition task, as well as a program for the monitoring of the codebook vectors at any time during the learning process. The rst version 1.0 of this program package was published in 1991 and since then the package has been updated regularly to include latest improvements in the LVQ implementations. This report that contains the last documentation was prepared for bibliographical purposes.

Exploratory Data Analysis by the Self-Organizing Map: Structures of Welfare and Poverty in the World

Abstract: The self organizing map SOM is a method that represents statistical data sets in an ordered fashion as a natural groundwork on which the distributions of the individual indicators in the set can be displayed and analyzed As a case study that instructs how to use the SOM to compare states of economic systems the standard of living of dif ferent countries is analyzed using the SOM Based on a great number of welfare indicators the SOM illustrates rather re ned relation ships between the countries two dimensionally This method is directly applicable to the nancial grading of companies too

Self-organizing maps of document collections: A new approach to interactive exploration

Abstract: Powerful methods for interactive exploration and search from collections of free-form textual documents are needed to manage the ever-increasing flood of digital information. In this article we present a method, WEBSOM, for automatic organization of full-text document collections using the self-organizing map (SOM) algorithm. The document collection is ordered onto a map in an unsupervised manner utilizing statistical information of short word contexts. The resulting ordered map where similar documents lie near each other thus presents a general view of the document space. With the aid of a suitable (WWW-based) interface, documents in interesting areas of the map can be browsed. The browsing can also be interactively extended to related topics, which appear in nearby areas on the map. Along with the method we present a case study of its use.

Self-organizing maps of document collections: a new approach to interactive exploration

Abstract: Powerful methods for interactive exploration and search from collections of free-form textual documents are needed to manage the ever-increasing flood of digital information. In this article we present a method, WEBSOM, for automatic organization of full-text document collections using the self-organizing map (SOM) algorithm. The document collection is ordered onto a map in an unsupervised manner utilizing statistical information of short word contexts. The resulting ordered map where similar documents lie near each other thus presents a general view of the document space. With the aid of a suitable (WWW-based) interface, documents in interesting areas of the map can be browsed. The browsing can also be interactively extended to related topics, which appear in nearby areas on the map. Along with the method we present a case study of its use.

Newsgroup Exploration with WEBSOM Method and Browsing Interface

Abstract: | The current availability of large collections of full-text documents in electronic form emphasizes the need for intelligent information retrieval techniques. Especially in the rapidly growing World Wide Web it is important to have methods for exploring miscellaneous document collections automatically. In the report, we introduce the WEBSOM method for this task. Self-Organizing Maps (SOMs) are used to position encoded documents onto a map that provides a general view into the text collection. The general view visualizes similarity relations between the documents on a map display, which can be utilized in exploring the material rather than having to rely on traditional search expressions. Similar documents become mapped close to each other. The potential of the WEBSOM method is demonstrated in a case study where articles from the Usenet newsgroup \comp.ai.neural-nets" are organized.

Emergence of invariant-feature detectors in the adaptive-subspace self-organizing map

Abstract: A new self-organizing map (SOM) architecture called the ASSOM (adaptive-subspace SOM) is shown to create sets of translation-invariant filters when randomly displaced or moving input patterns are used as training data. No analytical functional forms for these filters are thereby postulated. Different kinds of filters are formed by the ASSOM when pictures are rotated during learning, or when they are zoomed. The ASSOM can thus act as a learning feature-extraction stage for pattern recognizers, being able to adapt to many sensory environments and to many different transformation groups of patterns.

Very Large Two-Level SOM for the Browsing of Newsgroups

Abstract: On January 19, 1996 we published in the Internet a demo of how to use Self-Organizing Maps (SOMs) for the organization of large collections of full-text files. Later we added other newsgroups to the demo. It can be found at the address http://websom.hut.fi/websom/. In the present paper we describe the main features of this system, called the WEBSOM, as well as some newer developments of it.

Creating an Order in Digital Libraries with Self-Organizing Maps

Abstract: |Formulation of suitable search expressions for information retrieval from large full-text databases may currently require considerable eeorts. Changing the scope of the search when, e.g., too many or too few hits have been obtained, requires re-formulation of the search expression. For an alternative scheme we suggest an explorative full-text information retrieval method, where the Self-Organizing Map (SOM) algorithm is used to order documents based on their full textual contents. The visualized order can then be utilized for an explorative search or exploration of novel knowledge areas, whereby the scope can be changed interactively. The ordering of the documents is achieved by a two-level analysis: First, word categories are extracted from the text by a \semantic" SOM. Second, the textual context of the documents is encoded on the basis of the histograms of words formed on the word category map.

Self-Organizing Maps of Document Collections

Abstract: Apparatus for use with vehicle electro-pneumatic brake systems, said apparatus being characterized by a friction-free self-lapping valve device responsive to opposing control forces reflecting the degree of electrical dynamic braking and the degree of fluid pressure controlling the pneumatic friction braking for automatically maintaining the called-for overall braking effect of the dynamic and friction braking combined, said valve device being operable responsively to variation of dynamic braking effort for automatically maintaining the called-for electro-pneumatic braking relationship by compensatingly varying the degree of delivery fluid pressure for the pneumatic braking system.

Exploration of full-text databases with self-organizing maps

Abstract: Availability of large full-text document collections in electronic form has created a need for intelligent information retrieval techniques, especially the expanding World Wide Web which presupposes methods for systematic exploration of miscellaneous document collections. In this paper we introduce a new method, the WEBSOM, for this task. Self-organizing maps (SOMs) are used to represent documents on a map that provides an insightful view of the text collection. This view visualizes similarity relations between the documents, and the display can be utilized for orderly exploration of the material rather than having to rely on traditional search expressions. The complete WEBSOM method involves a two-level SOM architecture comprising of a word category map and a document map, and means for interactive exploration of the database.

Browsing digital libraries with the aid of self-organizing maps

Abstract: -Powerful methods for exploring and searching collections of free-form textual documents are needed to control the flood of digital information emerging from various sources. In this article we present a method, WEBSOM, for automatic organization of document collections based on full-text analysis using the Self-Organizing Map. The document collection is ordered on the map in such a way that similar documents lie near each other. The WWW-based user interface provides the basic functionalities necessary for intuitive exploration of the document space visualized with a map: moving on the map, zooming, and examining individual documents. We apply the method to the Usenet newsgroup comp.ai.neural-nets.