Transduction with Confidence and Credibility
Saunders, C., Gammerman, A. and Vovk, V. (1999) Transduction with Confidence and Credibility. At Sixteenth International Joint Conference on Artificial Intelligence (IJCAI '99) , 722-726.
In this paper we follow the same general ideology as in (Gammerman et. al, 1998), and describe a new transductive learning algorithm using Support Vector Machines. The algorithm presented provides confidence values for its predicted classifications of new examples. We also obtain a measure of ``credibility'' which serves as an indicator of the reliability of the data upon which we make our prediction. Experiments compare the new algorithm to a standard Support Vector Machine and other transductive methods which use Support Vector Machines, such as Vapnik's margin transduction. Empirical results show that the new algorithm not only produces confidence and credibility measures, but is comparable to, and sometimes exceeds the performance of the other algorithms.
|Item Type:||Conference or Workshop Item (Speech)|
|Divisions:||Faculty of Physical Sciences and Engineering > Electronics and Computer Science
|Date Deposited:||03 Mar 2004|
|Last Modified:||02 Mar 2012 13:40|
|Contributors:||Saunders, C. (Author)
Gammerman, A. (Author)
Vovk, V. (Author)
|Further Information:||Google Scholar|
|ISI Citation Count:||1|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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