Search (75 results, page 1 of 4)

Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.19

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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
Source: Proceedings of the 4th IEEE International Conference on Data Mining (ICDM 2004), 1-4 November 2004, Brighton, UK

HaCohen-Kerner, Y. et al.: Classification using various machine learning methods and combinations of key-phrases and visual features (2016) 0.05

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Date: 1. 2.2016 18:25:22
1. 2.2016 19:07:41

Jenkins, C.: Automatic classification of Web resources using Java and Dewey Decimal Classification (1998) 0.03

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Date: 1. 8.1996 22:08:06
Source: Computer networks and ISDN systems. 30(1998) nos.1/7, S.646-648

Bock, H.-H.: Datenanalyse zur Strukturierung und Ordnung von Information (1989) 0.03

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Pages: S.1-22

Khoo, C.S.G.; Ng, K.; Ou, S.: ¬An exploratory study of human clustering of Web pages (2003) 0.02
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Abstract

This study seeks to find out how human beings cluster Web pages naturally. Twenty Web pages retrieved by the Northem Light search engine for each of 10 queries were sorted by 3 subjects into categories that were natural or meaningful to them. lt was found that different subjects clustered the same set of Web pages quite differently and created different categories. The average inter-subject similarity of the clusters created was a low 0.27. Subjects created an average of 5.4 clusters for each sorting. The categories constructed can be divided into 10 types. About 1/3 of the categories created were topical. Another 20% of the categories relate to the degree of relevance or usefulness. The rest of the categories were subject-independent categories such as format, purpose, authoritativeness and direction to other sources. The authors plan to develop automatic methods for categorizing Web pages using the common categories created by the subjects. lt is hoped that the techniques developed can be used by Web search engines to automatically organize Web pages retrieved into categories that are natural to users. 1. Introduction The World Wide Web is an increasingly important source of information for people globally because of its ease of access, the ease of publishing, its ability to transcend geographic and national boundaries, its flexibility and heterogeneity and its dynamic nature. However, Web users also find it increasingly difficult to locate relevant and useful information in this vast information storehouse. Web search engines, despite their scope and power, appear to be quite ineffective. They retrieve too many pages, and though they attempt to rank retrieved pages in order of probable relevance, often the relevant documents do not appear in the top-ranked 10 or 20 documents displayed. Several studies have found that users do not know how to use the advanced features of Web search engines, and do not know how to formulate and re-formulate queries. Users also typically exert minimal effort in performing, evaluating and refining their searches, and are unwilling to scan more than 10 or 20 items retrieved (Jansen, Spink, Bateman & Saracevic, 1998). This suggests that the conventional ranked-list display of search results does not satisfy user requirements, and that better ways of presenting and summarizing search results have to be developed. One promising approach is to group retrieved pages into clusters or categories to allow users to navigate immediately to the "promising" clusters where the most useful Web pages are likely to be located. This approach has been adopted by a number of search engines (notably Northem Light) and search agents.

Date

12. 9.2004 9:56:22

Subramanian, S.; Shafer, K.E.: Clustering (2001) 0.02

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Date: 5. 5.2003 14:17:22

Reiner, U.: Automatische DDC-Klassifizierung von bibliografischen Titeldatensätzen (2009) 0.01

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Date: 22. 8.2009 12:54:24

Dubin, D.: Dimensions and discriminability (1998) 0.01

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Date: 22. 9.1997 19:16:05

Automatic classification research at OCLC (2002) 0.01

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Date: 5. 5.2003 9:22:09

Yoon, Y.; Lee, C.; Lee, G.G.: ¬An effective procedure for constructing a hierarchical text classification system (2006) 0.01

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Date: 22. 7.2006 16:24:52

Yi, K.: Automatic text classification using library classification schemes : trends, issues and challenges (2007) 0.01

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Date: 22. 9.2008 18:31:54

Kleinoeder, H.H.; Puzicha, J.: Automatische Katalogisierung am Beispiel einer Pilotanwendung (2002) 0.01

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Source: Info 7. 17(2002) H.1, S.19-21

Liu, R.-L.: Context recognition for hierarchical text classification (2009) 0.01

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Date: 22. 3.2009 19:11:54

Pfeffer, M.: Automatische Vergabe von RVK-Notationen mittels fallbasiertem Schließen (2009) 0.01

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Date: 22. 8.2009 19:51:28

Zhu, W.Z.; Allen, R.B.: Document clustering using the LSI subspace signature model (2013) 0.01

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Date: 23. 3.2013 13:22:36

Egbert, J.; Biber, D.; Davies, M.: Developing a bottom-up, user-based method of web register classification (2015) 0.01

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Date: 4. 8.2015 19:22:04

GERHARD : eine Spezialsuchmaschine für die Wissenschaft (1998) 0.01

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Date: 1. 4.2002 11:01:35

Shafer, K.E.: Automatic Subject Assignment via the Scorpion System (2001) 0.01

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Source: Journal of library administration. 34(2001) nos.1/2, S.187-189

Mengle, S.; Goharian, N.: Passage detection using text classification (2009) 0.01

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Date: 22. 3.2009 19:14:43

Liu, R.-L.: ¬A passage extractor for classification of disease aspect information (2013) 0.01

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Date: 28.10.2013 19:22:57

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