Search (5 results, page 1 of 1)

0.016009945 = product of:
  0.048029836 = sum of:
    0.048029836 = product of:
      0.09605967 = sum of:
        0.09605967 = weight(_text_:2002 in 1735) [ClassicSimilarity], result of:
          0.09605967 = score(doc=1735,freq=3.0), product of:
            0.20701107 = queryWeight, product of:
              4.28654 = idf(docFreq=1652, maxDocs=44218)
              0.048293278 = queryNorm
            0.46403155 = fieldWeight in 1735, product of:
              1.7320508 = tf(freq=3.0), with freq of:
                3.0 = termFreq=3.0
              4.28654 = idf(docFreq=1652, maxDocs=44218)
              0.0625 = fieldNorm(doc=1735)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Year

2002

0.008170041 = product of:
  0.024510123 = sum of:
    0.024510123 = product of:
      0.049020246 = sum of:
        0.049020246 = weight(_text_:2002 in 3888) [ClassicSimilarity], result of:
          0.049020246 = score(doc=3888,freq=2.0), product of:
            0.20701107 = queryWeight, product of:
              4.28654 = idf(docFreq=1652, maxDocs=44218)
              0.048293278 = queryNorm
            0.2368001 = fieldWeight in 3888, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.28654 = idf(docFreq=1652, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3888)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

We present a new technique called "t-SNE" that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images of objects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large data sets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of data sets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualizations produced by t-SNE are significantly better than those produced by the other techniques on almost all of the data sets.

0.0076423734 = product of:
  0.02292712 = sum of:
    0.02292712 = product of:
      0.04585424 = sum of:
        0.04585424 = weight(_text_:2002 in 1211) [ClassicSimilarity], result of:
          0.04585424 = score(doc=1211,freq=7.0), product of:
            0.20701107 = queryWeight, product of:
              4.28654 = idf(docFreq=1652, maxDocs=44218)
              0.048293278 = queryNorm
            0.22150622 = fieldWeight in 1211, product of:
              2.6457512 = tf(freq=7.0), with freq of:
                7.0 = termFreq=7.0
              4.28654 = idf(docFreq=1652, maxDocs=44218)
              0.01953125 = fieldNorm(doc=1211)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

In this article we present a method for retrieving documents from a digital library through a visual interface based on automatically generated concepts. We used a vocabulary generation algorithm to generate a set of concepts for the digital library and a technique called the max-min distance technique to cluster them. Additionally, the concepts were visualized in a spring embedding graph layout to depict the semantic relationship among them. The resulting graph layout serves as an aid to users for retrieving documents. An online archive containing the contents of D-Lib Magazine from July 1995 to May 2002 was used to test the utility of an implemented retrieval and visualization system. We believe that the method developed and tested can be applied to many different domains to help users get a better understanding of online document collections and to minimize users' cognitive load during execution of search tasks. Over the past few years, the volume of information available through the World Wide Web has been expanding exponentially. Never has so much information been so readily available and shared among so many people. Unfortunately, the unstructured nature and huge volume of information accessible over networks have made it hard for users to sift through and find relevant information. To deal with this problem, information retrieval (IR) techniques have gained more intensive attention from both industrial and academic researchers. Numerous IR techniques have been developed to help deal with the information overload problem. These techniques concentrate on mathematical models and algorithms for retrieval. Popular IR models such as the Boolean model, the vector-space model, the probabilistic model and their variants are well established.

Source

D-Lib magazine. 8(2002) no.10, x S

Year

2002

0.006543072 = product of:
  0.019629216 = sum of:
    0.019629216 = product of:
      0.03925843 = sum of:
        0.03925843 = weight(_text_:22 in 1289) [ClassicSimilarity], result of:
          0.03925843 = score(doc=1289,freq=2.0), product of:
            0.16911483 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.048293278 = queryNorm
            0.23214069 = fieldWeight in 1289, 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=1289)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Content

Vortrag anlässlich des Workshops: "Extending the multilingual capacity of The European Library in the EDL project Stockholm, Swedish National Library, 22-23 November 2007".

0.003271536 = product of:
  0.009814608 = sum of:
    0.009814608 = product of:
      0.019629216 = sum of:
        0.019629216 = weight(_text_:22 in 3035) [ClassicSimilarity], result of:
          0.019629216 = score(doc=3035,freq=2.0), product of:
            0.16911483 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.048293278 = queryNorm
            0.116070345 = fieldWeight in 3035, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0234375 = fieldNorm(doc=3035)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Content

As the team describe in a paper posted (http://arxiv.org/abs/1605.04951) on arXiv, they found that figures did indeed matter-but not all in the same way. An average paper in PubMed Central has about one diagram for every three pages and gets 1.67 citations. Papers with more diagrams per page and, to a lesser extent, plots per page tended to be more influential (on average, a paper accrued two more citations for every extra diagram per page, and one more for every extra plot per page). By contrast, including photographs and equations seemed to decrease the chances of a paper being cited by others. That agrees with a study from 2012, whose authors counted (by hand) the number of mathematical expressions in over 600 biology papers and found that each additional equation per page reduced the number of citations a paper received by 22%. This does not mean that researchers should rush to include more diagrams in their next paper. Dr Howe has not shown what is behind the effect, which may merely be one of correlation, rather than causation. It could, for example, be that papers with lots of diagrams tend to be those that illustrate new concepts, and thus start a whole new field of inquiry. Such papers will certainly be cited a lot. On the other hand, the presence of equations really might reduce citations. Biologists (as are most of those who write and read the papers in PubMed Central) are notoriously mathsaverse. If that is the case, looking in a physics archive would probably produce a different result.