Search (3 results, page 1 of 1)

  • × author_ss:"Fox, E.A."
  1. Fan, W.; Fox, E.A.; Pathak, P.; Wu, H.: ¬The effects of fitness functions an genetic programming-based ranking discovery for Web search (2004) 0.05 
    0.045352414 = product of:
  0.13605724 = sum of:
    0.12590174 = weight(_text_:ranking in 2239) [ClassicSimilarity], result of:
      0.12590174 = score(doc=2239,freq=6.0), product of:
        0.20271951 = queryWeight, product of:
          5.4090285 = idf(docFreq=537, maxDocs=44218)
          0.03747799 = queryNorm
        0.62106377 = fieldWeight in 2239, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          5.4090285 = idf(docFreq=537, maxDocs=44218)
          0.046875 = fieldNorm(doc=2239)
    0.0101555 = product of:
      0.030466499 = sum of:
        0.030466499 = weight(_text_:22 in 2239) [ClassicSimilarity], result of:
          0.030466499 = score(doc=2239,freq=2.0), product of:
            0.13124153 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03747799 = queryNorm
            0.23214069 = fieldWeight in 2239, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=2239)
      0.33333334 = coord(1/3)
  0.33333334 = coord(2/6)
    Abstract
    Genetic-based evolutionary learning algorithms, such as genetic algorithms (GAs) and genetic programming (GP), have been applied to information retrieval (IR) since the 1980s. Recently, GP has been applied to a new IR taskdiscovery of ranking functions for Web search-and has achieved very promising results. However, in our prior research, only one fitness function has been used for GP-based learning. It is unclear how other fitness functions may affect ranking function discovery for Web search, especially since it is weIl known that choosing a proper fitness function is very important for the effectiveness and efficiency of evolutionary algorithms. In this article, we report our experience in contrasting different fitness function designs an GP-based learning using a very large Web corpus. Our results indicate that the design of fitness functions is instrumental in performance improvement. We also give recommendations an the design of fitness functions for genetic-based information retrieval experiments.
    Date
    31. 5.2004 19:22:06
  2. Fan, W.; Luo, M.; Wang, L.; Xi, W.; Fox, E.A.: Tuning before feedback : combining ranking discovery and blind feedback for robust retrieval (2004) 0.02 
    0.020191502 = product of:
  0.121149 = sum of:
    0.121149 = weight(_text_:ranking in 4052) [ClassicSimilarity], result of:
      0.121149 = score(doc=4052,freq=2.0), product of:
        0.20271951 = queryWeight, product of:
          5.4090285 = idf(docFreq=537, maxDocs=44218)
          0.03747799 = queryNorm
        0.5976189 = fieldWeight in 4052, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.4090285 = idf(docFreq=537, maxDocs=44218)
          0.078125 = fieldNorm(doc=4052)
  0.16666667 = coord(1/6)
  3. Shen, R.; Wang, J.; Fox, E.A.: ¬A Lightweight Protocol between Digital Libraries and Visualization Systems (2002) 0.00 
    0.004787349 = product of:
  0.028724095 = sum of:
    0.028724095 = product of:
      0.08617228 = sum of:
        0.08617228 = weight(_text_:22 in 666) [ClassicSimilarity], result of:
          0.08617228 = score(doc=666,freq=4.0), product of:
            0.13124153 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03747799 = queryNorm
            0.6565931 = fieldWeight in 666, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.09375 = fieldNorm(doc=666)
      0.33333334 = coord(1/3)
  0.16666667 = coord(1/6)
    Date
    22. 2.2003 17:25:39
    22. 2.2003 18:15:14

Authors

Themes