Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
 
 
DownloadE-Mail
 Lokale TagsFreigabegeschichteDetailsÜbersicht
  Goal-oriented Methods and Meta Methods for Document Classification and their Parameter Tuning

Sizov, S., Siersdorfer, S., & Weikum, G. (2004). Goal-oriented Methods and Meta Methods for Document Classification and their Parameter Tuning. In CIKM 2004: proceedings of the Thirteenth Conference on Information and Knowledge Management (pp. 59-68). New York, USA: ACM.

Item is

 

einblenden: alle ausblenden: alle

Basisdaten

einblenden: ausblenden:
Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-000F-2AAF-9 Versions-Permalink: https://hdl.handle.net/11858/00-001M-0000-000F-2AB0-3
Genre: Konferenzbeitrag

Dateien

einblenden: Dateien
ausblenden: Dateien
:
cikm04-275-sizov.pdf (beliebiger Volltext), 202KB
 
Datei-Permalink:
-
Name:
cikm04-275-sizov.pdf
Beschreibung:
-
OA-Status:
Sichtbarkeit:
Privat
MIME-Typ / Prüfsumme:
application/pdf
Technische Metadaten:
Copyright Datum:
-
Copyright Info:
-
Lizenz:
-

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Sizov, Sergej1, Autor           
Siersdorfer, Stefan1, Autor           
Weikum, Gerhard1, Autor           
Evans, David A., Herausgeber
Gravano, Luis, Herausgeber
Herzog, Otthein, Herausgeber
Zhai, ChengXiang, Herausgeber
Ronthaler, Marc, Herausgeber
Affiliations:
1Databases and Information Systems, MPI for Informatics, Max Planck Society, ou_24018              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: Automatic text classification methods come with various calibration parameters such as thresholds for probabilities in Bayesian classifiers or for hyperplane distances in SVM classifiers. In a given application context these parameters should be set so as to meet the relative importance of various result quality metrics such as precision versus recall. In this paper we consider classifiers that can accept a document for a topic, reject it, or abstain. We aim to meet the application's goals in terms of accuracy (i.e., avoid false acceptances or rejections) and loss (i.e., limit the fraction of documents for which no decision is made). To this end we investigate restrictive forms of Support Vector Machine classifiers and we develop meta methods that split the training data into subsets for independently trained classifiers and then combine the results of these classifiers. These techniques tend to improve accuracy at the expense of document loss. We develop estimators that help to predict the accuracy and loss for a given setting of the methods' tuning parameters, and a methodology for efficiently deriving a setting that meets the application's goals. Our experiments confirm the practical viability of the approach.

Details

einblenden:
ausblenden:
Sprache(n): eng - English
 Datum: 2005-05-312004
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: New York, USA : ACM
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: eDoc: 231864
Anderer: Local-ID: C1256DBF005F876D-EE103EB2A21109DCC1256F9500333F20-SizovClass2004
 Art des Abschluß: -

Veranstaltung

einblenden:
ausblenden:
Titel: Untitled Event
Veranstaltungsort: Washington D.C., USA
Start-/Enddatum: 2004-11-08

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: CIKM 2004 : proceedings of the Thirteenth Conference on Information and Knowledge Management
Genre der Quelle: Konferenzband
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: New York, USA : ACM
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 59 - 68 Identifikator: -