Search (40 results, page 1 of 2)

  • × theme_ss:"Automatisches Klassifizieren"
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.10 
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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. Automatic classification research at OCLC (2002) 0.05 
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    Abstract
    OCLC enlists the cooperation of the world's libraries to make the written record of humankind's cultural heritage more accessible through electronic media. Part of this goal can be accomplished through the application of the principles of knowledge organization. We believe that cultural artifacts are effectively lost unless they are indexed, cataloged and classified. Accordingly, OCLC has developed products, sponsored research projects, and encouraged the participation in international standards communities whose outcome has been improved library classification schemes, cataloging productivity tools, and new proposals for the creation and maintenance of metadata. Though cataloging and classification requires expert intellectual effort, we recognize that at least some of the work must be automated if we hope to keep pace with cultural change
    Date
    5. 5.2003 9:22:09
  3. Khoo, C.S.G.; Ng, K.; Ou, S.: ¬An exploratory study of human clustering of Web pages (2003) 0.03 
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    Date
    12. 9.2004 9:56:22
    Series
    Advances in knowledge organization; vol.8
    Source
    Challenges in knowledge representation and organization for the 21st century: Integration of knowledge across boundaries. Proceedings of the 7th ISKO International Conference Granada, Spain, July 10-13, 2002. Ed.: M. López-Huertas
  4. Xu, Y.; Bernard, A.: Knowledge organization through statistical computation : a new approach (2009) 0.02 
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    Abstract
    Knowledge organization (KO) is an interdisciplinary issue which includes some problems in knowledge classification such as how to classify newly emerged knowledge. With the great complexity and ambiguity of knowledge, it is becoming sometimes inefficient to classify knowledge by logical reasoning. This paper attempts to propose a statistical approach to knowledge organization in order to resolve the problems in classifying complex and mass knowledge. By integrating the classification process into a mathematical model, a knowledge classifier, based on the maximum entropy theory, is constructed and the experimental results show that the classification results acquired from the classifier are reliable. The approach proposed in this paper is quite formal and is not dependent on specific contexts, so it could easily be adapted to the use of knowledge classification in other domains within KO.
    Source
    Knowledge organization. 36(2009) no.4, S.227-239
  5. Subramanian, S.; Shafer, K.E.: Clustering (2001) 0.02 
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    Date
    5. 5.2003 14:17:22
  6. Guerrero-Bote, V.P.; Moya Anegón, F. de; Herrero Solana, V.: Document organization using Kohonen's algorithm (2002) 0.02 
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    Abstract
    The classification of documents from a bibliographic database is a task that is linked to processes of information retrieval based on partial matching. A method is described of vectorizing reference documents from LISA which permits their topological organization using Kohonen's algorithm. As an example a map is generated of 202 documents from LISA, and an analysis is made of the possibilities of this type of neural network with respect to the development of information retrieval systems based on graphical browsing.
  7. Smiraglia, R.P.; Cai, X.: Tracking the evolution of clustering, machine learning, automatic indexing and automatic classification in knowledge organization (2017) 0.02 
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    Abstract
    A very important extension of the traditional domain of knowledge organization (KO) arises from attempts to incorporate techniques devised in the computer science domain for automatic concept extraction and for grouping, categorizing, clustering and otherwise organizing knowledge using mechanical means. Four specific terms have emerged to identify the most prevalent techniques: machine learning, clustering, automatic indexing, and automatic classification. Our study presents three domain analytical case analyses in search of answers. The first case relies on citations located using the ISKO-supported "Knowledge Organization Bibliography." The second case relies on works in both Web of Science and SCOPUS. Case three applies co-word analysis and citation analysis to the contents of the papers in the present special issue. We observe scholars involved in "clustering" and "automatic classification" who share common thematic emphases. But we have found no coherence, no common activity and no social semantics. We have not found a research front, or a common teleology within the KO domain. We also have found a lively group of authors who have succeeded in submitting papers to this special issue, and their work quite interestingly aligns with the case studies we report. There is an emphasis on KO for information retrieval; there is much work on clustering (which involves conceptual points within texts) and automatic classification (which involves semantic groupings at the meta-document level).
    Content
    Beitrag in einem Special Issue "New Trends for Knowledge Organization, Guest Editor: Renato Rocha Souza".
    Source
    Knowledge organization. 44(2017) no.3, S.215-233
  8. Koch, T.; Vizine-Goetz, D.: DDC and knowledge organization in the digital library : Research and development. Demonstration pages (1999) 0.02 
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    Content
    1. Increased Importance of Knowledge Organization in Internet Services - 2. Quality Subject Service and the role of classification - 3. Developing the DDC into a knowledge organization instrument for the digital library. OCLC site - 4. DESIRE's Barefoot Solutions of Automatic Classification - 5. Advanced Classification Solutions in DESIRE and CORC - 6. Future directions of research and development - 7. General references
  9. Golub, K.: Automated subject classification of textual documents in the context of Web-based hierarchical browsing (2011) 0.02 
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    Abstract
    While automated methods for information organization have been around for several decades now, exponential growth of the World Wide Web has put them into the forefront of research in different communities, within which several approaches can be identified: 1) machine learning (algorithms that allow computers to improve their performance based on learning from pre-existing data); 2) document clustering (algorithms for unsupervised document organization and automated topic extraction); and 3) string matching (algorithms that match given strings within larger text). Here the aim was to automatically organize textual documents into hierarchical structures for subject browsing. The string-matching approach was tested using a controlled vocabulary (containing pre-selected and pre-defined authorized terms, each corresponding to only one concept). The results imply that an appropriate controlled vocabulary, with a sufficient number of entry terms designating classes, could in itself be a solution for automated classification. Then, if the same controlled vocabulary had an appropriat hierarchical structure, it would at the same time provide a good browsing structure for the collection of automatically classified documents.
    Source
    Knowledge organization. 38(2011) no.3, S.230-244
  10. McKiernan, G.: Automated categorisation of Web resources : a profile of selected projects, research, products, and services (1996) 0.02 
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    Abstract
    Profiles several representative current efforts that apply established as well as more innovative methods of automated classification, organization or other method of categorisation of WWW resources
  11. Reiner, U.: Automatische DDC-Klassifizierung von bibliografischen Titeldatensätzen (2009) 0.02 
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    Date
    22. 8.2009 12:54:24
  12. HaCohen-Kerner, Y. et al.: Classification using various machine learning methods and combinations of key-phrases and visual features (2016) 0.02 
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    Date
    1. 2.2016 18:25:22
  13. Desale, S.K.; Kumbhar, R.: Research on automatic classification of documents in library environment : a literature review (2013) 0.02 
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    Abstract
    This paper aims to provide an overview of automatic classification research, which focuses on issues related to the automatic classification of documents in a library environment. The review covers literature published in mainstream library and information science studies. The review was done on literature published in both academic and professional LIS journals and other documents. This review reveals that basically three types of research are being done on automatic classification: 1) hierarchical classification using different library classification schemes, 2) text categorization and document categorization using different type of classifiers with or without using training documents, and 3) automatic bibliographic classification. Predominantly this research is directed towards solving problems of organization of digital documents in an online environment. However, very little research is devoted towards solving the problems of arrangement of physical documents.
    Source
    Knowledge organization. 40(2013) no.5, S.295-304
  14. Na, J.-C.; Sui, H.; Khoo, C.; Chan, S.; Zhou, Y.: Effectiveness of simple linguistic processing in automatic sentiment classification of product reviews (2004) 0.01 
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    Series
    Advances in knowledge organization; vol.9
    Source
    Knowledge organization and the global information society: Proceedings of the 8th International ISKO Conference 13-16 July 2004, London, UK. Ed.: I.C. McIlwaine
  15. Khoo, C.S.G.; Ou, S.: Machine versus human clustering of concepts across documents (2008) 0.01 
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    Series
    Advances in knowledge organization; vol.11
    Source
    Culture and identity in knowledge organization: Proceedings of the Tenth International ISKO Conference 5-8 August 2008, Montreal, Canada. Ed. by Clément Arsenault and Joseph T. Tennis
  16. Savic, D.: Automatic classification of office documents : review of available methods and techniques (1995) 0.01 
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    Abstract
    Classification of office documents is one of the administrative functions carried out by almost every organization and institution which sends and receives correspondence. Processing of this increasing amount of information coming and out going mail, in particular its classification, is time consuming and expensive. More and more organizations are seeking a solution for meeting this challenge by designing computer based systems for automatic classification. Examines the present status of available knowledge and methodology which can be used for automatic classification of office documents. Besides a review of classic methods and techniques, the focus id also placed on the application of artificial intelligence
  17. Rose, J.R.; Gasteiger, J.: HORACE: an automatic system for the hierarchical classification of chemical reactions (1994) 0.01 
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    Abstract
    Describes an automatic classification system for classifying chemical reactions. A detailed study of the classification of chemical reactions, based on topological and physicochemical features, is followed by an analysis of the hierarchical classification produced by the HORACE algorithm (Hierarchical Organization of Reactions through Attribute and Condition Eduction), which combines both approaches in a synergistic manner. The searching and updating of reaction hierarchies is demonstrated with the hierarchies produced for 2 data sets by the HORACE algorithm. Shows that reaction hierarchies provide an efficient access to reaction information and indicate the main reaction types for a given reaction scheme, define the scope of a reaction type, enable searchers to find unusual reactions, and can help in locating the reactions most relevant for a given problem
  18. Koch, T.; Vizine-Goetz, D.: Automatic classification and content navigation support for Web services : DESIRE II cooperates with OCLC (1998) 0.01 
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    Abstract
    Emerging standards in knowledge representation and organization are preparing the way for distributed vocabulary support in Internet search services. NetLab researchers are exploring several innovative solutions for searching and browsing in the subject-based Internet gateway, Electronic Engineering Library, Sweden (EELS). The implementation of the EELS service is described, specifically, the generation of the robot-gathered database 'All' engineering and the automated application of the Ei thesaurus and classification scheme. NetLab and OCLC researchers are collaborating to investigate advanced solutions to automated classification in the DESIRE II context. A plan for furthering the development of distributed vocabulary support in Internet search services is offered.
  19. 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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    Abstract
    Document clustering is an important tool for document collection organization and browsing. In real applications, some limited knowledge about cluster membership of a small number of documents is often available, such as some pairs of documents belonging to the same cluster. This kind of prior knowledge can be served as constraints for the clustering process. We integrate the constraints into the trace formulation of the sum of square Euclidean distance function of K-means. Then, the combined criterion function is transformed into trace maximization, which is further optimized by eigen-decomposition. Our experimental evaluation shows that the proposed semi-supervised clustering method can achieve better performance, compared to three existing methods.
  20. Bock, H.-H.: Datenanalyse zur Strukturierung und Ordnung von Information (1989) 0.01 
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    Pages
    S.1-22

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