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  • self-organizing map  (3)
  • learning vector quantization  (1)
  • 1
    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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  • 2
    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
    Library Location Call Number Volume/Issue/Year Availability
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    Paper (German National Licenses)
    Fulltext
  • 3
    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
    Library Location Call Number Volume/Issue/Year Availability
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    Paper (German National Licenses)
    Fulltext
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