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  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.11 
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    Content
    Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.
    Date
    8. 1.2013 10:22:32
  2. Dutta, B.: Ranganathan's elucidation of subject in the light of 'Infinity (8)' (2015) 0.08 
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    Abstract
    This paper reviews Ranganathan's description of subject from mathematical angle. Ranganathan was highly influenced by Nineteenth Century mathematician George Cantor and he used the concept of infinity in developing an axiomatic interpretation of subject. Majority of library scientists interpreted the concept of subject merely as a term or descriptor or heading to include the same in cataloguing and subject indexing. Some library scientists interpreted subject on the basis of document, i.e. from the angle of the concept of aboutness or epistemological potential of the document etc. Some people explained subject from the viewpoint of social, cultural or socio-cultural process. Attempts were made to describe subject from epistemological viewpoint. But S R Ranganathan was the first to develop an axiomatic concept of subject on its own. He built up an independent idea of subject that is ubiquitously pervasive with human cognition process. To develop the basic foundation of subject, he used the mathematical concepts of infinity and infinitesimal and construed the set of subjects or universe of subjects as continuous infinite universe. The subject may also exist in extremely micro-form, which was termed as spot subject and analogized with point, which is dimensionless having only an existence. The influence of Twentieth Century physicist George Gamow on Ranganathan's thought has also been discussed.
  3. Kleineberg, M.: Context analysis and context indexing : formal pragmatics in knowledge organization (2014) 0.08 
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    Source
    http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=5&ved=0CDQQFjAE&url=http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F3131107&ei=HzFWVYvGMsiNsgGTyoFI&usg=AFQjCNE2FHUeR9oQTQlNC4TPedv4Mo3DaQ&sig2=Rlzpr7a3BLZZkqZCXXN_IA&bvm=bv.93564037,d.bGg&cad=rja
  4. Azzaro, S.; Cleary, K.: One, two, three ... infinity : a computer assisted learning package for the first time user of SilverPlatter CD-ROMs (1994) 0.06 
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  5. Popper, K.R.: Three worlds : the Tanner lecture on human values. Deliverd at the University of Michigan, April 7, 1978 (1978) 0.06 
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    https%3A%2F%2Ftannerlectures.utah.edu%2F_documents%2Fa-to-z%2Fp%2Fpopper80.pdf&usg=AOvVaw3f4QRTEH-OEBmoYr2J_c7H
  6. Vetere, G.; Lenzerini, M.: Models for semantic interoperability in service-oriented architectures (2005) 0.05 
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    Content
    Vgl.: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5386707&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5386707.
  7. Hinich, M.J.; Molyneux, R.E.: Predicting information flows in network traffic (2003) 0.05 
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    Abstract
    Hinich and Molyneux review the literature of internet measurement and note three results consistently to be found in network traffic studies. These are "self-similarity," "long-range dependence," by which is meant that events in one time are correlated with events in a previous time and remain so through longer time periods than expected, and "heavy tails" by which they mean many small connections with low byte counts and a few long connections with large byte counts. The literature also suggests that conventional time series analysis is not helpful for network analysis. Using a single day's traffic at the Berkeley National Labs web server, cumulated TCP flows were collected, log transforms were used with the adding of .01 to all values allowing log transforms of the zero values, and providing a distribution that overcomes the heavy tail problem. However, Hinich's bicorrelation test for nonlinearity using overlapping moving windows found strong evidence of nonlinear structures. Time series analysis assumes linear systems theory and thus additivity and scalability. Spectral analysis should provide large peaks at the lowest frequencies if long range dependence is present since the power spectrum would go to infinity if the frequency goes to zero. This does not occur and so long range dependence must be questioned, at least until it is determined what effect other OSI layers may have on the TCP data.
  8. Egghe, L.; Rousseau, R.: ¬A measure for the cohesion of weighted networks (2003) 0.05 
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    Abstract
    Measurement of the degree of interconnectedness in graph like networks of hyperlinks or citations can indicate the existence of research fields and assist in comparative evaluation of research efforts. In this issue we begin with Egghe and Rousseau who review compactness measures and investigate the compactness of a network as a weighted graph with dissimilarity values characterizing the arcs between nodes. They make use of a generalization of the Botofogo, Rivlin, Shneiderman, (BRS) compaction measure which treats the distance between unreachable nodes not as infinity but rather as the number of nodes in the network. The dissimilarity values are determined by summing the reciprocals of the weights of the arcs in the shortest chain between two nodes where no weight is smaller than one. The BRS measure is then the maximum value for the sum of the dissimilarity measures less the actual sum divided by the difference between the maximum and minimum. The Wiener index, the sum of all elements in the dissimilarity matrix divided by two, is then computed for Small's particle physics co-citation data as well as the BRS measure, the dissimilarity values and shortest paths. The compactness measure for the weighted network is smaller than for the un-weighted. When the bibliographic coupling network is utilized it is shown to be less compact than the co-citation network which indicates that the new measure produces results that confirm to an obvious case.
  9. Mas, S.; Marleau, Y.: Proposition of a faceted classification model to support corporate information organization and digital records management (2009) 0.05 
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    Footnote
    Vgl.: http://ieeexplore.ieee.org/Xplore/login.jsp?reload=true&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F4755313%2F4755314%2F04755480.pdf%3Farnumber%3D4755480&authDecision=-203.
  10. Li, L.; Shang, Y.; Zhang, W.: Improvement of HITS-based algorithms on Web documents 0.05 
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    Content
    Vgl.: http%3A%2F%2Fdelab.csd.auth.gr%2F~dimitris%2Fcourses%2Fir_spring06%2Fpage_rank_computing%2Fp527-li.pdf. Vgl. auch: http://www2002.org/CDROM/refereed/643/.
  11. Zeng, Q.; Yu, M.; Yu, W.; Xiong, J.; Shi, Y.; Jiang, M.: Faceted hierarchy : a new graph type to organize scientific concepts and a construction method (2019) 0.05 
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    Content
    Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.
  12. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.05 
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    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  13. Farazi, M.: Faceted lightweight ontologies : a formalization and some experiments (2010) 0.04 
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    Content
    PhD Dissertation at International Doctorate School in Information and Communication Technology. Vgl.: https%3A%2F%2Fcore.ac.uk%2Fdownload%2Fpdf%2F150083013.pdf&usg=AOvVaw2n-qisNagpyT0lli_6QbAQ.
  14. Malsburg, C. von der: ¬The correlation theory of brain function (1981) 0.04 
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    http%3A%2F%2Fcogprints.org%2F1380%2F1%2FvdM_correlation.pdf&usg=AOvVaw0g7DvZbQPb2U7dYb49b9v_
  15. Ackermann, E.: Piaget's constructivism, Papert's constructionism : what's the difference? (2001) 0.04 
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    Content
    Vgl.: https://www.semanticscholar.org/paper/Piaget-%E2%80%99-s-Constructivism-%2C-Papert-%E2%80%99-s-%3A-What-%E2%80%99-s-Ackermann/89cbcc1e740a4591443ff4765a6ae8df0fdf5554. Darunter weitere Hinweise auf verwandte Beiträge. Auch unter: Learning Group Publication 5(2001) no.3, S.438.
  16. Stojanovic, N.: Ontology-based Information Retrieval : methods and tools for cooperative query answering (2005) 0.03 
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    Vgl.: http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F1627&ei=tAtYUYrBNoHKtQb3l4GYBw&usg=AFQjCNHeaxKkKU3-u54LWxMNYGXaaDLCGw&sig2=8WykXWQoDKjDSdGtAakH2Q&bvm=bv.44442042,d.Yms.
  17. Xiong, C.: Knowledge based text representations for information retrieval (2016) 0.03 
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    Content
    Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Language and Information Technologies. Vgl.: https%3A%2F%2Fwww.cs.cmu.edu%2F~cx%2Fpapers%2Fknowledge_based_text_representation.pdf&usg=AOvVaw0SaTSvhWLTh__Uz_HtOtl3.
  18. Jascó, P.: Searching for images by similarity online (1998) 0.03 
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    Date
    29.11.2004 13:03:22
    Source
    Online. 22(1998) no.6, S.99-102
  19. Hawking, D.; Robertson, S.: On collection size and retrieval effectiveness (2003) 0.03 
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    Date
    14. 8.2005 14:22:22
  20. Buzydlowski, J.W.; White, H.D.; Lin, X.: Term Co-occurrence Analysis as an Interface for Digital Libraries (2002) 0.03 
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    Date
    22. 2.2003 17:25:39
    22. 2.2003 18:16:22

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