Search (1 results, page 1 of 1)

  • × author_ss:"Bernard, A."
  • × theme_ss:"Automatisches Klassifizieren"
  1. Xu, Y.; Bernard, A.: Knowledge organization through statistical computation : a new approach (2009) 0.00
    0.0024857575 = product of:
      0.004971515 = sum of:
        0.004971515 = product of:
          0.00994303 = sum of:
            0.00994303 = weight(_text_:a in 3252) [ClassicSimilarity], result of:
              0.00994303 = score(doc=3252,freq=12.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.18723148 = fieldWeight in 3252, product of:
                  3.4641016 = tf(freq=12.0), with freq of:
                    12.0 = termFreq=12.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046875 = fieldNorm(doc=3252)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Knowledge organization (KO) is an interdisciplinary issue which includes some problems in knowledge classification such as how to classify newly emerged knowledge. With the great complexity and ambiguity of knowledge, it is becoming sometimes inefficient to classify knowledge by logical reasoning. This paper attempts to propose a statistical approach to knowledge organization in order to resolve the problems in classifying complex and mass knowledge. By integrating the classification process into a mathematical model, a knowledge classifier, based on the maximum entropy theory, is constructed and the experimental results show that the classification results acquired from the classifier are reliable. The approach proposed in this paper is quite formal and is not dependent on specific contexts, so it could easily be adapted to the use of knowledge classification in other domains within KO.
    Type
    a