Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
 
 
DownloadE-Mail
 Lokale TagsFreigabegeschichteDetailsÜbersicht
  Semi-Supervised Protein Classification using Cluster Kernels

Weston, J., Leslie, C., Zhou, D., Elisseeff, A., & Noble, W. (2004). Semi-Supervised Protein Classification using Cluster Kernels. Advances in Neural Information Processing Systems 16, 595-602.

Item is

 

einblenden: alle ausblenden: alle

Basisdaten

einblenden: ausblenden:
Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D8FD-C Versions-Permalink: https://hdl.handle.net/21.11116/0000-0005-5383-9
Genre: Konferenzbeitrag

Externe Referenzen

einblenden:
ausblenden:
externe Referenz:
https://papers.nips.cc/paper/2496-semi-supervised-protein-classification-using-cluster-kernels.pdf (Verlagsversion)
Beschreibung:
-
OA-Status:

Urheber

einblenden:
ausblenden:
 Urheber:
Weston, J1, 2, Autor           
Leslie, C, Autor
Zhou, D1, 2, Autor           
Elisseeff, A1, 2, Autor           
Noble, WS, Autor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: A key issue in supervised protein classification is the representation of input sequences of amino acids. Recent work using string kernels for protein data has achieved state-of-the-art classification performance. However, such representations are based only on labeled data --- examples with known 3D structures, organized into structural classes --- while in practice, unlabeled data is far more plentiful. In this work, we develop simple and scalable cluster kernel techniques for incorporating unlabeled data into the representation of protein sequences. We show that our methods greatly improve the classification performance of string kernels and outperform standard approaches for using unlabeled data, such as adding close homologs of the positive examples to the training data. We achieve equal or superior performance to previously presented cluster kernel methods while achieving far greater computational efficiency.

Details

einblenden:
ausblenden:
Sprache(n):
 Datum: 2004-06
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: BibTex Citekey: 2336
 Art des Abschluß: -

Veranstaltung

einblenden:
ausblenden:
Titel: Seventeenth Annual Conference on Neural Information Processing Systems (NIPS 2003)
Veranstaltungsort: Vancouver, BC, Canada
Start-/Enddatum: 2003-12-09 - 2003-12-11

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: Advances in Neural Information Processing Systems 16
Genre der Quelle: Zeitschrift
 Urheber:
Thrun, S, Herausgeber
Saul, LK, Herausgeber
Schölkopf, B1, Herausgeber           
Affiliations:
1 Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794            
Ort, Verlag, Ausgabe: Cambridge, MA, USA : MIT Press
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 595 - 602 Identifikator: ISBN: 0-262-20152-6