Transductive Inference for Text Classification using Support Vector Machines
Proceedings of ICML-99, 16th International Conference on Machine Learning, Seite 200--209. Bled, SL, Morgan Kaufmann Publishers, San Francisco, US, (1999)

This paper introduces Transductive Support Vector Machines (TSVMs) for text classifi­ cation. While regular Support Vector Ma­ chines (SVMs) try to induce a general deci­ sion function for a learning task, Transduc­ tive Support Vector Machines take into ac­ count a particular test set and try to mini­ mize misclassifications of just those particu­ lar examples. The paper presents an anal­ ysis of why TSVMs are well suited for text classification. These theoretical findings are supported by experiments on three test col­ lections. The experiments show substantial improvements over inductive methods, espe­ cially for small training sets, cutting the num­ ber of labeled training examples down to a twentieth on some tasks. This work also pro­ poses an algorithm for training TSVMs effi­ ciently, handling 10,000 examples and more.
  • @benz
Diese Publikation wurde noch nicht bewertet.

Durchschnittliche Benutzerbewertung0,0 von 5.0 auf Grundlage von 0 Rezensionen
    Bitte melden Sie sich an um selbst Rezensionen oder Kommentare zu erstellen.