Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

 
 
DownloadE-Mail
  Multi-Classification by Categorical Features via Clustering

Seldin, Y. (2008). Multi-Classification by Categorical Features via Clustering. Talk presented at 25th International Conference on Machine Learning (ICML 2008). Helsinki, Finland.

Item is

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Seldin, Y1, Autor           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: We derive a generalization bound for multi-classification schemes based on grid clustering in categorical parameter product spaces. Grid clustering partitions the parameter space in the form of a Cartesian product of partitions for each of the parameters. The derived bound provides a means to evaluate clustering solutions in terms of the generalization power of a built-on classifier. For classification based on a single feature the bound serves to find a globally optimal classification rule. Comparison of the generalization power of individual features can then be used for feature ranking. Our experiments show that in this role the bound is much more precise than mutual information or normalized correlation indices.

Details

einblenden:
ausblenden:
Sprache(n):
 Datum: 2008-06
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: URI: http://videolectures.net/icml08_seldin_mcc/
BibTex Citekey: 6601
 Art des Abschluß: -

Veranstaltung

einblenden:
ausblenden:
Titel: 25th International Conference on Machine Learning (ICML 2008)
Veranstaltungsort: Helsinki, Finland
Start-/Enddatum: -

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle

einblenden: