Search (65 results, page 1 of 4)

  • × theme_ss:"Automatisches Klassifizieren"
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.20 
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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. Savic, D.: Designing an expert system for classifying office documents (1994) 0.04 
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    Abstract
    Can records management benefit from artificial intelligence technology, in particular from expert systems? Gives an answer to this question by showing an example of a small scale prototype project in automatic classification of office documents. Project methodology and basic elements of an expert system's approach are elaborated to give guidelines to potential users of this promising technology
    Source
    Records management quarterly. 28(1994) no.3, S.20-29
  3. Savic, D.: Automatic classification of office documents : review of available methods and techniques (1995) 0.02 
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    Source
    Records management quarterly. 29(1995) no.4, S.3-18
  4. Liu, R.-L.: Context recognition for hierarchical text classification (2009) 0.02 
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    Abstract
    Information is often organized as a text hierarchy. A hierarchical text-classification system is thus essential for the management, sharing, and dissemination of information. It aims to automatically classify each incoming document into zero, one, or several categories in the text hierarchy. In this paper, we present a technique called CRHTC (context recognition for hierarchical text classification) that performs hierarchical text classification by recognizing the context of discussion (COD) of each category. A category's COD is governed by its ancestor categories, whose contents indicate contextual backgrounds of the category. A document may be classified into a category only if its content matches the category's COD. CRHTC does not require any trials to manually set parameters, and hence is more portable and easier to implement than other methods. It is empirically evaluated under various conditions. The results show that CRHTC achieves both better and more stable performance than several hierarchical and nonhierarchical text-classification methodologies.
    Date
    22. 3.2009 19:11:54
  5. Kwok, K.L.: ¬The use of titles and cited titles as document representations for automatic classification (1975) 0.02 
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    Source
    Information processing and management. 11(1975), S.201-206
  6. Wu, M.; Fuller, M.; Wilkinson, R.: Using clustering and classification approaches in interactive retrieval (2001) 0.02 
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    Source
    Information processing and management. 37(2001) no.3, S.459-484
  7. Automatische Klassifikation und Extraktion in Documentum (2005) 0.02 
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    Content
    "LCI Comprend ist ab sofort als integriertes Modul für EMCs Content Management System Documentum verfügbar. LCI (Learning Computers International GmbH) hat mit Unterstützung von neeb & partner diese Technologie zur Dokumentenautomation transparent in Documentum integriert. Dies ist die erste bekannte Lösung für automatische, lernende Klassifikation und Extraktion, die direkt auf dem Documentum Datenbestand arbeitet und ohne zusätzliche externe Steuerung auskommt. Die LCI Information Capture Services (ICS) dienen dazu, jegliche Art von Dokument zu klassifizieren und Information daraus zu extrahieren. Das Dokument kann strukturiert, halbstrukturiert oder unstrukturiert sein. Somit können beispielsweise gescannte Formulare genauso verarbeitet werden wie Rechnungen oder E-Mails. Die Extraktions- und Klassifikationsvorschriften und die zu lernenden Beispieldokumente werden einfach interaktiv zusammengestellt und als XML-Struktur gespeichert. Zur Laufzeit wird das Projekt angewendet, um unbekannte Dokumente aufgrund von Regeln und gelernten Beispielen automatisch zu indexieren. Dokumente können damit entweder innerhalb von Documentum oder während des Imports verarbeitet werden. Der neue Server erlaubt das Einlesen von Dateien aus dem Dateisystem oder direkt von POPS-Konten, die Analyse der Dokumente und die automatische Erzeugung von Indexwerten bei der Speicherung in einer Documentum Ablageumgebung. Diese Indexwerte, die durch inhaltsbasierte, auch mehrthematische Klassifikation oder durch Extraktion gewonnen wurden, werden als vordefinierte Attribute mit dem Documentum-Objekt abgelegt. Handelt es sich um ein gescanntes Dokument oder ein Fax, wird automatisch die integrierte Volltext-Texterkennung durchgeführt."
    Footnote
    Kontakt: LCI GmbH, Freiburger Str. 16, 16,79199 Kirchzarten, Tel.: (0 76 61) 9 89 961o, Fax: (01212) 5 37 48 29 36, info@lci-software.com, www.lci-software.com
  8. Kwon, O.W.; Lee, J.H.: Text categorization based on k-nearest neighbor approach for web site classification (2003) 0.02 
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    Date
    27.12.2007 17:32:29
    Source
    Information processing and management. 39(2003) no.1, S.25-44
  9. Major, R.L.; Ragsdale, C.T.: ¬An aggregation approach to the classification problem using multiple prediction experts (2000) 0.02 
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    Source
    Information processing and management. 36(2000) no.4, S.683-696
  10. Krellenstein, M.: Document classification at Northern Light (1999) 0.02 
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    Footnote
    Vortrag bei: Search engines and beyond: developing efficient knowledge management systems; 1999 Search engine Meeting, Boston, MA, April 19-20 1999
  11. Guerrero-Bote, V.P.; Moya Anegón, F. de; Herrero Solana, V.: Document organization using Kohonen's algorithm (2002) 0.01 
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    Source
    Information processing and management. 38(2002) no.1, S.79-89
  12. Classification, automation, and new media : Proceedings of the 24th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Passau, March 15 - 17, 2000 (2002) 0.01 
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    Abstract
    Given the huge amount of information in the internet and in practically every domain of knowledge that we are facing today, knowledge discovery calls for automation. The book deals with methods from classification and data analysis that respond effectively to this rapidly growing challenge. The interested reader will find new methodological insights as well as applications in economics, management science, finance, and marketing, and in pattern recognition, biology, health, and archaeology.
    Content
    Data Analysis, Statistics, and Classification.- Pattern Recognition and Automation.- Data Mining, Information Processing, and Automation.- New Media, Web Mining, and Automation.- Applications in Management Science, Finance, and Marketing.- Applications in Medicine, Biology, Archaeology, and Others.- Author Index.- Subject Index.
  13. Losee, R.M.: Text windows and phrases differing by discipline, location in document, and syntactic structure (1996) 0.01 
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    Source
    Information processing and management. 32(1996) no.6, S.747-767
  14. Miyamoto, S.: Information clustering based an fuzzy multisets (2003) 0.01 
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    Source
    Information processing and management. 39(2003) no.2, S.195-213
  15. Hu, G.; Zhou, S.; Guan, J.; Hu, X.: Towards effective document clustering : a constrained K-means based approach (2008) 0.01 
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    Information processing and management. 44(2008) no.4, S.1397-1409
  16. Wu, K.J.; Chen, M.-C.; Sun, Y.: Automatic topics discovery from hyperlinked documents (2004) 0.01 
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    Information processing and management. 40(2004) no.2, S.239-255
  17. Liu, R.-L.: Dynamic category profiling for text filtering and classification (2007) 0.01 
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    Information processing and management. 43(2007) no.1, S.154-168
  18. Yoon, Y.; Lee, G.G.: Efficient implementation of associative classifiers for document classification (2007) 0.01 
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