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  • 1
    Electronic Resource
    Electronic Resource
    Emergence of invariant-feature detectors in the adaptive-subspace self-organizing map (1996)
    Springer
    Biological cybernetics 75 (1996), S. 281-291 
    Details
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: 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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/s004220050295
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    Self-organized formation of topologically correct feature maps (1982)
    Springer
    Biological cybernetics 43 (1982), S. 59-69 
    Details
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract This work contains a theoretical study and computer simulations of a new self-organizing process. The principal discovery is that in a simple network of adaptive physical elements which receives signals from a primary event space, the signal representations are automatically mapped onto a set of output responses in such a way that the responses acquire the same topological order as that of the primary events. In other words, a principle has been discovered which facilitates the automatic formation of topologically correct maps of features of observable events. The basic self-organizing system is a one- or two-dimensional array of processing units resembling a network of threshold-logic units, and characterized by short-range lateral feedback between neighbouring units. Several types of computer simulations are used to demonstrate the ordering process as well as the conditions under which it fails.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00337288
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    Paper (German National Licenses)
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  • 3
    Electronic Resource
    Electronic Resource
    Websom for Textual Data Mining (1999)
    Springer
    Artificial intelligence review 13 (1999), S. 345-364 
    Details
    ISSN: 1573-7462
    Keywords: data mining ; document filtering ; exploratory data analysis ; information retrieval ; self-organizing map ; SOM ; text document collection ; WEBSOM
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract New methods that are user-friendly and efficient are needed for guidanceamong the masses of textual information available in the Internet and theWorld Wide Web. We have developed a method and a tool called the WEBSOMwhich utilizes the self-organizing map algorithm (SOM) for organizing largecollections of text documents onto visual document maps. The approach toprocessing text is statistically oriented, computationally feasible, andscalable – over a million text documents have been ordered on a single map.In the article we consider different kinds of information needs and tasksregarding organizing, visualizing, searching, categorizing and filteringtextual data. Furthermore, we discuss and illustrate with examples howdocument maps can aid in these situations. An example is presented wherea document map is utilized as a tool for visualizing and filtering a stream ofincoming electronic mail messages.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1006586221250
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    Paper (German National Licenses)
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  • 4
    Electronic Resource
    Electronic Resource
    Analysis of a simple self-organizing process (1982)
    Springer
    Biological cybernetics 44 (1982), S. 135-140 
    Details
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract This paper contains mathematical results relating to a recently discovered self-organizing process. In particular, an analysis of two partial processes is presented. In the first one, a set of numerical representations assumes the correct order, and in the second one the final map of the representations converges to its asymptotic form.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00317973
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    Paper (German National Licenses)
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  • 5
    Electronic Resource
    Electronic Resource
    Self-Organizing Maps and Learning Vector Quantization for Feature Sequences (1999)
    Springer
    Neural processing letters 10 (1999), S. 151-159 
    Details
    ISSN: 1573-773X
    Keywords: learning vector quantization ; self-organizing map ; sequence processing
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The Self-Organizing Map (SOM) and Learning Vector Quantization (LVQ) algorithms are constructed in this work for variable-length and warped feature sequences. The novelty is to associate an entire feature vector sequence, instead of a single feature vector, as a model with each SOM node. Dynamic time warping is used to obtain time-normalized distances between sequences with different lengths. Starting with random initialization, ordered feature sequence maps then ensue, and Learning Vector Quantization can be used to fine tune the prototype sequences for optimal class separation. The resulting SOM models, the prototype sequences, can then be used for the recognition as well as synthesis of patterns. Good results have been obtained in speaker-independent speech recognition.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1018741720065
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  • 6
    Electronic Resource
    Electronic Resource
    Fast Evolutionary Learning with Batch-Type Self-Organizing Maps (1999)
    Springer
    Neural processing letters 9 (1999), S. 153-162 
    Details
    ISSN: 1573-773X
    Keywords: batch map ; evolutionary learning ; genetic algorithm ; self-organizing map ; SOM
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Although no distance function over the input data is definable, it is still possible to implement the self-organizing map (SOM) process using evolutionary-learning operations. The process can be made to converge more rapidly when the probabilistic trials of conventional evolutionary learning are replaced by averaging using the so-called Batch Map version of the self-organizing map. Although no other condition or metric than a fitness function between the input samples and the models is assumed, an order in the map that complies with the ‘functional similarity’ of the models can be seen to emerge. There exist two modes of use of this new principle: representation of nonmetric input data distributions by models that may have variable structures, and fast generation of evolutionary cycles that resemble those defined by the genetic algorithms. The spatial order in the array of models can be utilized for finding more uniform variations, such as crossings between functionally similar models.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1018681526204
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    Paper (German National Licenses)
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  • 7
    Book
    Book
    Self-organizing maps; 30 (1997)
    Berlin u.a. :Springer,
    Details
    Title: Self-organizing maps; 30
    Author: Kohonen, Teuvo
    Edition: 2. Auflage
    Publisher: Berlin u.a. :Springer,
    Year of publication: 1997
    Pages: 426 S.
    Series Statement: Springer series in information sciences 30
    Type of Medium: Book
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  • 8
    Book
    Book
    Self-organizing maps; 30 (2000)
    Berlin u.a. :Springer,
    Details
    Title: Self-organizing maps; 30
    Author: Kohonen, Teuvo
    Edition: 3rd ed.
    Publisher: Berlin u.a. :Springer,
    Year of publication: 2000
    Pages: 501 S.
    Series Statement: Springer series in information sciences 30
    Type of Medium: Book
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