Search (1432 results, page 1 of 72)

  • × year_i:[2000 TO 2010}
  1. McIlwaine, I.C.; Williamson, N.J.: Class 61 - Medicine : restructuring progress 2000 (2000) 0.17 
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    Source
    Extensions and corrections to the UDC. 22(2000), S.49-75
  2. Srimurugan, A.: ¬An expert systems for generation of UDC class numbers : an investigation (2000) 0.17 
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    Source
    Extensions and corrections to the UDC. 22(2000), S.25-30
  3. Subrahmanyam, B.: Library of Congress Classification numbers : issues of consistency and their implications for union catalogs (2006) 0.14 
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    Abstract
    This study examined Library of Congress Classification (LCC)-based class numbers assigned to a representative sample of 200 titles in 52 American library systems to determine the level of consistency within and across those systems. The results showed that under the condition that a library system has a title, the probability of that title having the same LCC-based class number across library systems is greater than 85 percent. An examination of 121 titles displaying variations in class numbers among library systems showed certain titles (for example, multi-foci titles, titles in series, bibliographies, and fiction) lend themselves to alternate class numbers. Others were assigned variant numbers either due to latitude in the schedules or for reasons that cannot be pinpointed. With increasing dependence on copy cataloging, the size of such variations may continue to decrease. As the preferred class number with its alternates represents a title more fully than just the preferred class number, this paper argues for continued use of alternates by library systems and for finding a method to link alternate class numbers to preferred class numbers for enriched subject access through local and union catalogs.
    Date
    10. 9.2000 17:38:22
  4. Sen, B.K.: DDC readymade : a treasury to 15,000 readymade DDC class numbers relating to Indian subjects, English language and literature (2001) 0.14 
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    Abstract
    The book provides around 15,000 readymade class numbers relating to Indian subjects and English language and literature. It has covered four Indic religions, namely Buddhism, Hinduism, Jainism and Sikhism. For each of these religions class numbers for around fifty subdivisions of each of the impotent South Asian Languages like Assamese, Bengali, English Gujarati, Hindi, Kannada, Malayalam, Marathi, Nepali, Oriya, Punjabi , Sanskrit Sindhi, Sinhalese, Tamil Telugu and Urdu have been provided. he numbers for other South Asian languages noticed in DDC have also been included. For literature of each of the aforementioned languages the class numbers for around 450 subdivisions have been provided. Around 750 class cumbers have been provided for English language and literature. More than 250 class numbers have been provided under the heading History including all the periods for such subjects as Architecture, Conservation of resources, Cultural contact, Economic development, Economic geography, Elections, Elementary educational, Fauna, Flora, Folk, dancing, Folk literature, Folk supply, General clubs, Geography Geology, Higher education, Journalism, Local public debt, Local taxes, Museums, Newspapers, Organizations, Painting and paintings Political situation, Postal organizations, Public administration, Public finance, Public policy on education, Revenue, Secondary education, Social problems/social welfare, Social welfare Problems and services, State taxes, Statistics, Strikes, and so on with more than 40 geographical subdivisions. In addition to this, more than a dozen class number have been provided for all the Indian states and union territories. Necessary instructions have been given as to how class numbers are to be located, and expanded when necessary.
    Object
    DDC-22
  5. Ferris, A.M.: If you buy it, will they use it? : a case study on the use of Classification web (2006) 0.11 
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    Abstract
    This paper presents a study conducted at the University of Colorado at Boulder (CU-Boulder) to assess the extent to which its catalogers were using Classification Web (Class Web), the subscription-based, online cataloging documentation resource provided by the Library of Congress. In addition, this paper will explore assumptions made by management regarding CU-Boulder catalogers' use of the product, possible reasons for the lower-than-expected use, and recommendations for promoting a more efficient and cost-effective use of Class Web at other institutions similar to CU-Boulder.
    Date
    10. 9.2000 17:38:22
  6. 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
  7. Taniguchi, S.: Recording evidence in bibliographic records and descriptive metadata (2005) 0.10 
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    Abstract
    In this article recording evidence for data values in addition to the values themselves in bibliographic records and descriptive metadata is proposed, with the aim of improving the expressiveness and reliability of those records and metadata. Recorded evidence indicates why and how data values are recorded for elements. Recording the history of changes in data values is also proposed, with the aim of reinforcing recorded evidence. First, evidence that can be recorded is categorized into classes: identifiers of rules or tasks, action descriptions of them, and input and output data of them. Dates of recording values and evidence are an additional class. Then, the relative usefulness of evidence classes and also levels (i.e., the record, data element, or data value level) to which an individual evidence class is applied, is examined. Second, examples that can be viewed as recorded evidence in existing bibliographic records and current cataloging rules are shown. Third, some examples of bibliographic records and descriptive metadata with notes of evidence are demonstrated. Fourth, ways of using recorded evidence are addressed.
    Date
    18. 6.2005 13:16:22
  8. Alex, H.; Heiner-Freiling, M.: Melvil (2005) 0.09 
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    Abstract
    Ab Januar 2006 wird Die Deutsche Bibliothek ein neues Webangebot mit dem Namen Melvil starten, das ein Ergebnis ihres Engagements für die DDC und das Projekt DDC Deutsch ist. Der angebotene Webservice basiert auf der Übersetzung der 22. Ausgabe der DDC, die im Oktober 2005 als Druckausgabe im K. G. Saur Verlag erscheint. Er bietet jedoch darüber hinausgehende Features, die den Klassifizierer bei seiner Arbeit unterstützen und erstmals eine verbale Recherche für Endnutzer über DDCerschlossene Titel ermöglichen. Der Webservice Melvil gliedert sich in drei Anwendungen: - MelvilClass, - MelvilSearch und - MelvilSoap.
    Object
    Melvil Class
  9. Yoon, Y.; Lee, C.; Lee, G.G.: ¬An effective procedure for constructing a hierarchical text classification system (2006) 0.09 
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    Abstract
    In text categorization tasks, classification on some class hierarchies has better results than in cases without the hierarchy. Currently, because a large number of documents are divided into several subgroups in a hierarchy, we can appropriately use a hierarchical classification method. However, we have no systematic method to build a hierarchical classification system that performs well with large collections of practical data. In this article, we introduce a new evaluation scheme for internal node classifiers, which can be used effectively to develop a hierarchical classification system. We also show that our method for constructing the hierarchical classification system is very effective, especially for the task of constructing classifiers applied to hierarchy tree with a lot of levels.
    Date
    22. 7.2006 16:24:52
  10. McIlwaine, I.C.; Williamson, N.J.: Class 61 - Medicine : restructuring progress in 2004 (2004) 0.08 
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    Abstract
    Proposal for the restructuring of UDC class 617.6/.9 - Nervous system. Neurology
  11. Méndez, E.; López, L.M.; Siches, A.; Bravo, A.G.: DCMF: DC & Microformats, a good marriage (2008) 0.07 
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    Abstract
    This report introduces the Dublin Core Microformats (DCMF) project, a new way to use the DC element set within X/HTML. The DC microformats encode explicit semantic expressions in an X/HTML webpage, by using a specific list of terms for values of the attributes "rev" and "rel" for <a> and <link> elements, and "class" and "id" of other elements. Microformats can be easily processed by user agents and software, enabling a high level of interoperability. These characteristics are crucial for the growing number of social applications allowing users to participate in the Web 2.0 environment as information creators and consumers. This report reviews the origins of microformats; illustrates the coding of DC microformats using the Dublin Core Metadata Gen tool, and a Firefox extension for extraction and visualization; and discusses the benefits of creating Web services utilizing DC microformats.
    Source
    Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas
  12. Sehgal, R.L.: ¬An introduction to Dewey Decimal Classification (2005) 0.07 
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    Abstract
    This book describes the structure of the DDC. The various chapters discuss the history; structure; notation; Devices such as: common auxiliaries, special auxiliaries available for the extension of the class and the isolate; and mnemonics. The parts explain the methodology of classifying documents. It is divided into two main divisions. The scope of the subjects covered under the various sub-divisions along with the methodology of number building has been discussed under each main division
    Date
    28. 2.2008 17:22:52
    Object
    DDC-22
  13. McIlwaine, I.C.; Williamson, N.J.: Class 61 - Medicine : restructuring progress 2002 (2002) 0.06 
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  14. Heidorn, P.B.; Wei, Q.: Automatic metadata extraction from museum specimen labels (2008) 0.06 
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    Abstract
    This paper describes the information properties of museum specimen labels and machine learning tools to automatically extract Darwin Core (DwC) and other metadata from these labels processed through Optical Character Recognition (OCR). The DwC is a metadata profile describing the core set of access points for search and retrieval of natural history collections and observation databases. Using the HERBIS Learning System (HLS) we extract 74 independent elements from these labels. The automated text extraction tools are provided as a web service so that users can reference digital images of specimens and receive back an extended Darwin Core XML representation of the content of the label. This automated extraction task is made more difficult by the high variability of museum label formats, OCR errors and the open class nature of some elements. In this paper we introduce our overall system architecture, and variability robust solutions including, the application of Hidden Markov and Naïve Bayes machine learning models, data cleaning, use of field element identifiers, and specialist learning models. The techniques developed here could be adapted to any metadata extraction situation with noisy text and weakly ordered elements.
    Source
    Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas
  15. Li, T.; Zhu, S.; Ogihara, M.: Text categorization via generalized discriminant analysis (2008) 0.06 
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    Abstract
    Text categorization is an important research area and has been receiving much attention due to the growth of the on-line information and of Internet. Automated text categorization is generally cast as a multi-class classification problem. Much of previous work focused on binary document classification problems. Support vector machines (SVMs) excel in binary classification, but the elegant theory behind large-margin hyperplane cannot be easily extended to multi-class text classification. In addition, the training time and scaling are also important concerns. On the other hand, other techniques naturally extensible to handle multi-class classification are generally not as accurate as SVM. This paper presents a simple and efficient solution to multi-class text categorization. Classification problems are first formulated as optimization via discriminant analysis. Text categorization is then cast as the problem of finding coordinate transformations that reflects the inherent similarity from the data. While most of the previous approaches decompose a multi-class classification problem into multiple independent binary classification tasks, the proposed approach enables direct multi-class classification. By using generalized singular value decomposition (GSVD), a coordinate transformation that reflects the inherent class structure indicated by the generalized singular values is identified. Extensive experiments demonstrate the efficiency and effectiveness of the proposed approach.
  16. Saving the time of the library user through subject access innovation : Papers in honor of Pauline Atherton Cochrane (2000) 0.06 
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    Abstract
    Pauline Atherton Cochrane has been contributing to library and information science for fifty years. Think of it-from mid-century to the millennium, from ENIAC (practically) to Internet 11 (almost here). What a time to be in our field! Her work an indexing, subject access, and the user-oriented approach had immediate and sustained impact, and she continues to be one of our most heavily cited authors (see, JASIS, 49[4], 327-55) and most beloved personages. This introduction includes a few words about my own experiences with Pauline as well as a short summary of the contributions that make up this tribute. A review of the curriculum vita provided at the end of this publication Shows that Pauline Cochrane has been involved in a wide variety of work. As Marcia Bates points out in her note (See below), Pauline was (and is) a role model, but I will always think of her as simply the best teacher 1 ever had. In 1997, I entered the University of Illinois Graduate School of Library and Information Science as a returning mid-life student; my previous doctorate had not led to a full-time job and I was re-tooling. I was not sure what 1 would find in library school, and the introductory course attended by more than 100 students from widely varied backgrounds had not yet convinced me I was in the right place. Then, one day, Pauline gave a guest lecture an the digital library in my introductory class. I still remember it. She put up some notes-a few words clustered an the blackboard with some circles and directional arrows-and then she gave a free, seemingly extemporaneous, but riveting narrative. She set out a vision for ideal information exchange in the digital environment but noted a host of practical concerns, issues, and potential problems that required (demanded!) continued human intervention. The lecture brought that class and the entire semester's work into focus; it created tremendous excitement for the future of librarianship. 1 saw that librarians and libraries would play an active role. I was in the right place.
    Date
    22. 9.1997 19:16:05
  17. Stamatatos, E.: Author identification : using text sampling to handle the class imbalance problem (2008) 0.05 
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    Abstract
    Authorship analysis of electronic texts assists digital forensics and anti-terror investigation. Author identification can be seen as a single-label multi-class text categorization problem. Very often, there are extremely few training texts at least for some of the candidate authors or there is a significant variation in the text-length among the available training texts of the candidate authors. Moreover, in this task usually there is no similarity between the distribution of training and test texts over the classes, that is, a basic assumption of inductive learning does not apply. In this paper, we present methods to handle imbalanced multi-class textual datasets. The main idea is to segment the training texts into text samples according to the size of the class, thus producing a fairer classification model. Hence, minority classes can be segmented into many short samples and majority classes into less and longer samples. We explore text sampling methods in order to construct a training set according to a desirable distribution over the classes. Essentially, by text sampling we provide new synthetic data that artificially increase the training size of a class. Based on two text corpora of two languages, namely, newswire stories in English and newspaper reportage in Arabic, we present a series of authorship identification experiments on various multi-class imbalanced cases that reveal the properties of the presented methods.
  18. Ferris, A.M.: Results of an expanded survey on the use of Classification Web : they will use it, if you buy it! (2009) 0.05 
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    Abstract
    This paper presents the results of a survey examining the extent to which working catalogers use Classification Web, the Library of Congress' online resource for subject heading and classification documentation. An earlier survey analyzed Class Web's usefulness on an institutional level. This broader survey expands on that analysis and provides information on such questions as: what types of institutions subscribe to Class Web; what are the reasons for using Class Web when performing original or copy cataloging; and what other resources do catalogers use for classification/subject heading analysis?
  19. Alessandri, S.: Classificare la letteratura : la classe 800 nelle edizioni della DDC (2002) 0.05 
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    Footnote
    Übers. d. Titels: Classifying literature: the 800 class in Dewey Decimal Classification editions
  20. Brenes, D.F.: Classification schemes (2006) 0.05 
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    Abstract
    The article reviews the Web site Classification schemes ACM Computing Classification Systems from the Association for Computing Machinery, available at http://www.acm.org/class/.

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