Borgelt, C.; Kruse, R.: Unsicheres Wissen nutzen (2002)
0.04
0.042605944 = product of:
0.12781782 = sum of:
0.110492796 = weight(_text_:wissen in 1104) [ClassicSimilarity], result of:
0.110492796 = score(doc=1104,freq=4.0), product of:
0.1639626 = queryWeight, product of:
4.3128977 = idf(docFreq=1609, maxDocs=44218)
0.038016807 = queryNorm
0.67389023 = fieldWeight in 1104, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
4.3128977 = idf(docFreq=1609, maxDocs=44218)
0.078125 = fieldNorm(doc=1104)
0.017325027 = product of:
0.05197508 = sum of:
0.05197508 = weight(_text_:29 in 1104) [ClassicSimilarity], result of:
0.05197508 = score(doc=1104,freq=2.0), product of:
0.13373125 = queryWeight, product of:
3.5176873 = idf(docFreq=3565, maxDocs=44218)
0.038016807 = queryNorm
0.38865322 = fieldWeight in 1104, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5176873 = idf(docFreq=3565, maxDocs=44218)
0.078125 = fieldNorm(doc=1104)
0.33333334 = coord(1/3)
0.33333334 = coord(2/6)
- Abstract
- Probabilistische Schlussfolgerungsnetze sind ein probates Mittel, unsicheres Wissen sauber und mathematisch fundiert zu verarbeiten. In neuerer Zeit wurden Verfahren entwickelt, um sie automatisch aus Beispieldaten zu erlernen
- Date
- 31.12.1996 19:29:41