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  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.10 
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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. Kleineberg, M.: Context analysis and context indexing : formal pragmatics in knowledge organization (2014) 0.06 
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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
  3. Rauber, A.: Digital preservation in data-driven science : on the importance of process capture, preservation and validation (2012) 0.05 
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    Abstract
    Current digital preservation is strongly biased towards data objects: digital files of document-style objects, or encapsulated and largely self-contained objects. To provide authenticity and provenance information, comprehensive metadata models are deployed to document information on an object's context. Yet, we claim that simply documenting an objects context may not be sufficient to ensure proper provenance and to fulfill the stated preservation goals. Specifically in e-Science and business settings, capturing, documenting and preserving entire processes may be necessary to meet the preservation goals. We thus present an approach for capturing, documenting and preserving processes, and means to assess their authenticity upon re-execution. We will discuss options as well as limitations and open challenges to achieve sound preservation, speci?cally within scientific processes.
  4. Popper, K.R.: Three worlds : the Tanner lecture on human values. Deliverd at the University of Michigan, April 7, 1978 (1978) 0.05 
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    Source
    https%3A%2F%2Ftannerlectures.utah.edu%2F_documents%2Fa-to-z%2Fp%2Fpopper80.pdf&usg=AOvVaw3f4QRTEH-OEBmoYr2J_c7H
  5. Vetere, G.; Lenzerini, M.: Models for semantic interoperability in service-oriented architectures (2005) 0.04 
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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.
  6. Egghe, L.: Properties of the n-overlap vector and n-overlap similarity theory (2006) 0.04 
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    Abstract
    In the first part of this article the author defines the n-overlap vector whose coordinates consist of the fraction of the objects (e.g., books, N-grams, etc.) that belong to 1, 2, , n sets (more generally: families) (e.g., libraries, databases, etc.). With the aid of the Lorenz concentration theory, a theory of n-overlap similarity is conceived together with corresponding measures, such as the generalized Jaccard index (generalizing the well-known Jaccard index in case n 5 2). Next, the distributional form of the n-overlap vector is determined assuming certain distributions of the object's and of the set (family) sizes. In this section the decreasing power law and decreasing exponential distribution is explained for the n-overlap vector. Both item (token) n-overlap and source (type) n-overlap are studied. The n-overlap properties of objects indexed by a hierarchical system (e.g., books indexed by numbers from a UDC or Dewey system or by N-grams) are presented in the final section. The author shows how the results given in the previous section can be applied as well as how the Lorenz order of the n-overlap vector is respected by an increase or a decrease of the level of refinement in the hierarchical system (e.g., the value N in N-grams).
  7. Bordogna, G.; Pagani, M.: ¬A flexible content-based image retrieval model and a customizable system for the retrieval of shapes (2010) 0.04 
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    Abstract
    The authors describe a flexible model and a system for content-based image retrieval of objects' shapes. Flexibility is intended as the possibility of customizing the system behavior to the user's needs and perceptions. This is achieved by allowing users to modify the retrieval function. The system implementing this model uses multiple representations to characterize some macroscopic characteristics of the objects shapes. Specifically, the shape indexes describe the global features of the object's contour (represented by the Fourier coefficients), the contour's irregularities (represented by the multifractal spectrum), and the presence of concavities and convexities (represented by the contour scale space distribution). During a query formulation, the user can specify both the preference for the macroscopic shape aspects that he or she considers meaningful for the retrieval, and the desired level of accuracy of the matching, which means that the visual query shape must be considered with a given tolerance in representing the desired shapes. The evaluation experiments showed that this system can be suited to different retrieval behaviors, and that, generally, the combination of the multiple shape representations increases both recall and precision with respect to the application of any single representation.
  8. Mas, S.; Marleau, Y.: Proposition of a faceted classification model to support corporate information organization and digital records management (2009) 0.04 
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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.
  9. Li, L.; Shang, Y.; Zhang, W.: Improvement of HITS-based algorithms on Web documents 0.04 
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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/.
  10. 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) 0.04 
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    Content
    Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.
  11. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.04 
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    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  12. Jascó, P.: Searching for images by similarity online (1998) 0.04 
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    Date
    29.11.2004 13:03:22
    Source
    Online. 22(1998) no.6, S.99-102
  13. Hawking, D.; Robertson, S.: On collection size and retrieval effectiveness (2003) 0.04 
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    Date
    14. 8.2005 14:22:22
  14. 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
  15. Dahlberg, I.: Conceptual definitions for INTERCONCEPT (1981) 0.03 
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    Source
    International classification. 8(1981), S.16-22
  16. Pietris, M.K.D.: LCSH update (1988) 0.03 
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    Source
    Cataloguing Australia. 13(1988), S.19-22
  17. Serial cataloguing : modern perspectives and international developments (1992) 0.03 
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    Source
    Serials librarian. 22(1992), nos.3/4
  18. Woods, W.A.: What's important about knowledge representation? (1983) 0.03 
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    Source
    Computer. 16(1983) no.10, S.22-27
  19. Smith, G.: Newspapers on CD-ROM (1992) 0.03 
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    Serials. 5(1992) no.3, S.17-22
  20. Panizzi, A.K.C.B.: Passages in my official life (1871) 0.03 
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    Date
    22. 7.2007 12:05:26
    22. 7.2007 12:08:24

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Years

Types

  • a 1930
  • m 151
  • s 98
  • el 70
  • b 31
  • r 10
  • x 8
  • i 3
  • n 2
  • p 2
  • h 1
  • More… Less…

Themes

Subjects

Classifications