Search (2 results, page 1 of 1)

  • × theme_ss:"Automatisches Klassifizieren"
  • × year_i:[2020 TO 2030}
  1. Han, K.; Rezapour, R.; Nakamura, K.; Devkota, D.; Miller, D.C.; Diesner, J.: ¬An expert-in-the-loop method for domain-specific document categorization based on small training data (2023) 0.01
    0.006136797 = product of:
      0.024547188 = sum of:
        0.024547188 = weight(_text_:j in 967) [ClassicSimilarity], result of:
          0.024547188 = score(doc=967,freq=2.0), product of:
            0.1398433 = queryWeight, product of:
              3.1774964 = idf(docFreq=5010, maxDocs=44218)
              0.044010527 = queryNorm
            0.17553353 = fieldWeight in 967, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1774964 = idf(docFreq=5010, maxDocs=44218)
              0.0390625 = fieldNorm(doc=967)
      0.25 = coord(1/4)
    
  2. Illing, S.: Automatisiertes klinisches Codieren (2021) 0.00
    0.0024262597 = product of:
      0.009705039 = sum of:
        0.009705039 = product of:
          0.019410077 = sum of:
            0.019410077 = weight(_text_:der in 419) [ClassicSimilarity], result of:
              0.019410077 = score(doc=419,freq=2.0), product of:
                0.098309256 = queryWeight, product of:
                  2.2337668 = idf(docFreq=12875, maxDocs=44218)
                  0.044010527 = queryNorm
                0.19743896 = fieldWeight in 419, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  2.2337668 = idf(docFreq=12875, maxDocs=44218)
                  0.0625 = fieldNorm(doc=419)
          0.5 = coord(1/2)
      0.25 = coord(1/4)
    
    Abstract
    Die in diesem Artikel vorgestellte Bachelorarbeit behandelt die Ergebnisse einer Shared Task im Bereich eHealth. Es wird untersucht, ob die Klassifikationsgenauigkeit ausgewählter klinischer Codiersysteme durch den Einsatz von Ensemble-Methoden verbessert werden kann. Entscheidend dafür sind die Werte der Evaluationsmaße Mean Average Precision und F1-Maß.

Languages