Search (3 results, page 1 of 1)

0.029039085 = product of:
  0.05807817 = sum of:
    0.04145074 = weight(_text_:digitale in 4041) [ClassicSimilarity], result of:
      0.04145074 = score(doc=4041,freq=2.0), product of:
        0.1818122 = queryWeight, product of:
          5.158747 = idf(docFreq=690, maxDocs=44218)
          0.035243478 = queryNorm
        0.22798656 = fieldWeight in 4041, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.158747 = idf(docFreq=690, maxDocs=44218)
          0.03125 = fieldNorm(doc=4041)
    0.016627433 = weight(_text_:information in 4041) [ClassicSimilarity], result of:
      0.016627433 = score(doc=4041,freq=24.0), product of:
        0.06186915 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.035243478 = queryNorm
        0.2687516 = fieldWeight in 4041, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.03125 = fieldNorm(doc=4041)
  0.5 = coord(2/4)

Abstract

Class-tested and coherent, this textbook teaches information retrieval, including web search, text classification, and text clustering from basic concepts. Ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students. Slides and additional exercises are available for lecturers. - This book provides what Salton and Van Rijsbergen both failed to achieve. Even more important, unlike some other books in IR, the authors appear to care about making the theory as accessible as possible to the reader, on occasion including short primers to certain topics or choosing to explain difficult concepts using simplified approaches. Its coverage [is] excellent, the quality of writing high and I was surprised how much I learned from reading it. I think the online resources are impressive.

Content

Inhalt: Boolean retrieval - The term vocabulary & postings lists - Dictionaries and tolerant retrieval - Index construction - Index compression - Scoring, term weighting & the vector space model - Computing scores in a complete search system - Evaluation in information retrieval - Relevance feedback & query expansion - XML retrieval - Probabilistic information retrieval - Language models for information retrieval - Text classification & Naive Bayes - Vector space classification - Support vector machines & machine learning on documents - Flat clustering - Hierarchical clustering - Matrix decompositions & latent semantic indexing - Web search basics - Web crawling and indexes - Link analysis Vgl. die digitale Fassung unter: http://nlp.stanford.edu/IR-book/pdf/irbookprint.pdf.

LCSH

Information retrieval

RSWK

Dokumentverarbeitung / Information Retrieval / Abfrageverarbeitung (GBV)
Information Retrieval / Einführung (BVB)

Subject

Dokumentverarbeitung / Information Retrieval / Abfrageverarbeitung (GBV)
Information Retrieval / Einführung (BVB)
Information retrieval

0.0025980363 = product of:
  0.010392145 = sum of:
    0.010392145 = weight(_text_:information in 4763) [ClassicSimilarity], result of:
      0.010392145 = score(doc=4763,freq=6.0), product of:
        0.06186915 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.035243478 = queryNorm
        0.16796975 = fieldWeight in 4763, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4763)
  0.25 = coord(1/4)

Abstract

Mosaic for Windows is an informative book on how to use the most popular Internet navigation tool ever developed. By focussing on the PC Windows version of Mosaic (NCSA, AIR Mosaic, and Spyglass), including Web browsers like NetScape, WinWeb and WebSurfer, this book will provide an easy-to-follow guide to using a PC and Mosaic to browse, collect, and discover information and resources across the entire electronic world.

RSWK

Information Retrieval (BVB)

Subject

Information Retrieval (BVB)

0.0011999814 = product of:
  0.0047999257 = sum of:
    0.0047999257 = weight(_text_:information in 1640) [ClassicSimilarity], result of:
      0.0047999257 = score(doc=1640,freq=2.0), product of:
        0.06186915 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.035243478 = queryNorm
        0.0775819 = fieldWeight in 1640, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.03125 = fieldNorm(doc=1640)
  0.25 = coord(1/4)

Abstract

Ein Grundproblem der rechnergestützten Erstellung, Bearbeitung und Archivierung von Dokumenten ist die unzureichende Wiedergabe von Inhalt und Struktur durch die heute verbreiteten Dokumentformate und Seitenbeschreibungssprachen. Die Standard Generalized Markup Language löst dieses Problem druch die präzise und flexible Beschreibung der Struktur von Dokumenten. SGML ermöglicht dadurch die vielseitige Nutzung der in Dokumenten enthaltenen Information für elektronische Publikationen, Hypertext-Systeme, Online-Dokumente, aber auch für die klassischen Formen der Publikation in Buch, Zeitschrift und Loseblattwerk. Darüber hinaus dient der ISO-Standard SGML als Austauschformat in heterogenen DV-Umgebungen. Das Buch vermittelt sowohl die für die Entscheidungsträger notwendigen Informationen als auch das Basiswissen für den Entwickler. Es gibt einen praxisorientierten Einstieg in Vorteile und Anwendungsgebiete von SGML, behandelt anhand vieler Beispiele die verschiedenen Bestandteile von Dokument-Typ-Definitionen und gibt einen Überblick über die verfügbare SGML-Software. Für Anwender von SGML und Entwickler von SGML-Anwendungen, DV-Fachleute in Verlagen, technische Dokumentare und allgemein an Dokumentverarbeitung Interessierte.