Search (52 results, page 1 of 3)

  • × theme_ss:"Retrievalalgorithmen"
  1. Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983) 0.03 
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    Date
    30. 3.2001 13:32:22
    Source
    Computer journal. 26(1983), S.239-246
  2. Dominich, S.: Mathematical foundations of information retrieval (2001) 0.02 
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    Abstract
    This book offers a comprehensive and consistent mathematical approach to information retrieval (IR) without which no implementation is possible, and sheds an entirely new light upon the structure of IR models. It contains the descriptions of all IR models in a unified formal style and language, along with examples for each, thus offering a comprehensive overview of them. The book also creates mathematical foundations and a consistent mathematical theory (including all mathematical results achieved so far) of IR as a stand-alone mathematical discipline, which thus can be read and taught independently. Also, the book contains all necessary mathematical knowledge on which IR relies, to help the reader avoid searching different sources. The book will be of interest to computer or information scientists, librarians, mathematicians, undergraduate students and researchers whose work involves information retrieval.
    Date
    22. 3.2008 12:26:32
    LCSH
    Computer science / Mathematics
    Subject
    Computer science / Mathematics
  3. Chang, C.-H.; Hsu, C.-C.: Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval (1998) 0.02 
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    Date
    1. 8.1996 22:08:06
    Source
    Computer networks and ISDN systems. 30(1998) nos.1/7, S.621-623
  4. Kelledy, F.; Smeaton, A.F.: Signature files and beyond (1996) 0.01 
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    Source
    Information retrieval: new systems and current research. Proceedings of the 16th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Drymen, Scotland, 22-23 Mar 94. Ed.: R. Leon
  5. Klas, C.-P.; Fuhr, N.; Schaefer, A.: Evaluating strategic support for information access in the DAFFODIL system (2004) 0.01 
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    Date
    16.11.2008 16:22:48
    Series
    Lecture notes in computer science; vol.3232
  6. Rijsbergen, C.J. van: ¬A fast hierarchic clustering algorithm (1970) 0.01 
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    Source
    Computer journal. 13(1970), S.324-326
  7. Zhang, D.; Dong, Y.: ¬An effective algorithm to rank Web resources (2000) 0.01 
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    Source
    Computer networks. 33(2000) nos.1/6, S.449-455
  8. Finding anything in the billion page Web : are algorithms the key? (1999) 0.01 
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    Source
    Computer networks. 31(1999) no.11-16, S.1760-1761
  9. Berry, M.W.; Browne, M.: Understanding search engines : mathematical modeling and text retrieval (1999) 0.01 
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    Abstract
    This book discusses many of the key design issues for building search engines and emphazises the important role that applied mathematics can play in improving information retrieval. The authors discuss not only important data structures, algorithms, and software but also user-centered issues such as interfaces, manual indexing, and document preparation. They also present some of the current problems in information retrieval that many not be familiar to applied mathematicians and computer scientists and some of the driving computational methods (SVD, SDD) for automated conceptual indexing
    LCSH
    Text processing (Computer science)
    Subject
    Text processing (Computer science)
  10. Computational information retrieval (2001) 0.01 
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    Abstract
    This volume contains selected papers that focus on the use of linear algebra, computational statistics, and computer science in the development of algorithms and software systems for text retrieval. Experts in information modeling and retrieval share their perspectives on the design of scalable but precise text retrieval systems, revealing many of the challenges and obstacles that mathematical and statistical models must overcome to be viable for automated text processing. This very useful proceedings is an excellent companion for courses in information retrieval, applied linear algebra, and applied statistics. Computational Information Retrieval provides background material on vector space models for text retrieval that applied mathematicians, statisticians, and computer scientists may not be familiar with. For graduate students in these areas, several research questions in information modeling are exposed. In addition, several case studies concerning the efficacy of the popular Latent Semantic Analysis (or Indexing) approach are provided.
  11. Chang, R.: Keyword searching and indexing (1993) 0.01 
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    Abstract
    Explains how a computer indexing system works. Reviews fundamentals of how data are stored and retrieved by computers. Describes B-Tree and B+-Tree indexing structures. Gives basic keyword searching techniques that the user must apply to make use of the indexing programs. The demand for keyword retrieval is increasing and librarians should expect to see the keyword-indexing feature become commonly available
  12. Hofferer, M.: Heuristic search in information retrieval (1994) 0.01 
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    Source
    Information retrieval: new systems and current research. Proceedings of the 15th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Glasgow 1993. Ed.: Ruben Leon
  13. Sembok, T.M.T.; Rijsbergen, C.J. van: IMAGING: a relevant feedback retrieval with nearest neighbour clusters (1994) 0.01 
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    Information retrieval: new systems and current research. Proceedings of the 15th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Glasgow 1993. Ed.: Ruben Leon
  14. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.01 
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    Source
    Information processing and management. 22(1986) no.6, S.465-476
  15. Lalmas, M.: XML retrieval (2009) 0.01 
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    LCSH
    Query languages (Computer science)
    Subject
    Query languages (Computer science)
  16. Keen, M.: Query reformulation in ranked output interaction (1994) 0.01 
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    Source
    Information retrieval: new systems and current research. Proceedings of the 15th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Glasgow 1993. Ed.: Ruben Leon
  17. Carpineto, C.; Romano, G.: Information retrieval through hybrid navigation of lattice representations (1996) 0.01 
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    Source
    International journal of human-computer studies. 45(1996) no.5, S.553-578
  18. Wollf, J.G.: ¬A scalable technique for best-match retrieval of sequential information using metrics-guided search (1994) 0.01 
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    Abstract
    Describes a new technique for retrieving information by finding the best match or matches between a textual query and a textual database. The technique uses principles of beam search with a measure of probability to guide the search and prune the search tree. Unlike many methods for comparing strings, the method gives a set of alternative matches, graded by the quality of the matching. The new technique is embodies in a software simulation SP21 which runs on a conventional computer. Presnts examples showing best-match retrieval of information from a textual database. Presents analytic and emprirical evidence on the performance of the technique. It lends itself well to parallel processing. Discusses planned developments
  19. Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000) 0.01 
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    Date
    25. 8.2005 17:42:22
  20. Wilhelmy, A.: Phonetische Ähnlichkeitssuche in Datenbanken (1991) 0.00 
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    Abstract
    In dialoggesteuerten Systemen zur Informationswiedergewinnung (Information Retrieval Systems, IRS) kann man - vergröbernd - das Wechselspiel zwischen Mensch und Computer als iterativen Prozess zur Erhöhung von Genauigkeit (Precision) auf der einen und Vollständigkeit (Recall) der Nachweise auf der anderen Seite verstehen. Vorgestellt wird ein maschinell anwendbares Verfahren, das auf phonologische Untersuchungen des Sprachwissenschaftlers Nikolaj S. Trubetzkoy (1890-1938) zurückgeht. In den Grundzügen kann es erheblich zur Verbesserung der Nachweisvollständigkeit beitragen. Dadurch, daß es die 'Ähnlichkeitsumgebungen' von Suchbegriffen in die Recherche mit einbezieht, zeigt es sich vor allem für Systeme mit koordinativer maschineller Indexierung als vorteilhaft. Bei alphabetischen Begriffen erweist sich die Einführung eines solchen zunächst nur auf den Benutzer hin orientierten Verfahrens auch aus technischer Sicht als günstig, da damit die Anzahl der Zugriffe bei den Suchvorgängen auch für große Datenvolumina niedrig gehalten werden kann

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