Search (4 results, page 1 of 1)

0.0022540248 = product of:
  0.03381037 = sum of:
    0.03381037 = sum of:
      0.010253613 = weight(_text_:information in 2239) [ClassicSimilarity], result of:
        0.010253613 = score(doc=2239,freq=6.0), product of:
          0.050870337 = queryWeight, product of:
            1.7554779 = idf(docFreq=20772, maxDocs=44218)
            0.028978055 = queryNorm
          0.20156369 = fieldWeight in 2239, product of:
            2.4494898 = tf(freq=6.0), with freq of:
              6.0 = termFreq=6.0
            1.7554779 = idf(docFreq=20772, maxDocs=44218)
            0.046875 = fieldNorm(doc=2239)
      0.023556758 = weight(_text_:22 in 2239) [ClassicSimilarity], result of:
        0.023556758 = score(doc=2239,freq=2.0), product of:
          0.101476215 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.028978055 = 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)
  0.06666667 = coord(1/15)

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

Source

Journal of the American Society for Information Science and technology. 55(2004) no.7, S.628-636

2.79068E-4 = product of:
  0.0041860198 = sum of:
    0.0041860198 = product of:
      0.0083720395 = sum of:
        0.0083720395 = weight(_text_:information in 4577) [ClassicSimilarity], result of:
          0.0083720395 = score(doc=4577,freq=4.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.16457605 = fieldWeight in 4577, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=4577)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

Abstract

Image Ranking is one of the key problems in information science research area. However, most current methods focus on increasing the performance, leaving the semantic gap problem, which refers to the learned ranking models are hard to be understood, remaining intact. Therefore, in this article, we aim at learning an interpretable ranking model to tackle the semantic gap in fine-grained image ranking. We propose to combine attribute-based representation and online passive-aggressive (PA) learning based ranking models to achieve this goal. Besides, considering the highly localized instances in fine-grained image ranking, we introduce a supervised constrained clustering method to gather class-balanced training instances for local PA-based models, and incorporate the learned local models into a unified probabilistic framework. Extensive experiments on the benchmark demonstrate that the proposed framework outperforms state-of-the-art methods in terms of accuracy and speed.

Source

Journal of the Association for Information Science and Technology. 69(2018) no.12, S.1488-1501

1.9733087E-4 = product of:
  0.002959963 = sum of:
    0.002959963 = product of:
      0.005919926 = sum of:
        0.005919926 = weight(_text_:information in 3455) [ClassicSimilarity], result of:
          0.005919926 = score(doc=3455,freq=2.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.116372846 = fieldWeight in 3455, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=3455)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

Source

Journal of the American Society for Information Science and Technology. 56(2005) no.6, S.571-583

1.6444239E-4 = product of:
  0.0024666358 = sum of:
    0.0024666358 = product of:
      0.0049332716 = sum of:
        0.0049332716 = weight(_text_:information in 2069) [ClassicSimilarity], result of:
          0.0049332716 = score(doc=2069,freq=2.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.09697737 = fieldWeight in 2069, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2069)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

Source

Information processing and management. 44(2008) no.2, S.893-909