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Semi-Supervised Support Vector Machines and Application to Spam Filtering




After introducing the semi-supervised support vector machine (aka TSVM for "transductive SVM"), a few popular training strategies are briefly presented. Then the assumptions underlying semi-supervised learning are reviewed. Finally, two modern TSVM optimization techniques are applied to the spam filtering data sets of the workshop; it is shown that they can achieve excellent results, if the problem of the data being non-iid can be handled properly.

Author(s): Zien, A.
Year: 2006
Month: September
Day: 22

Department(s): Empirical Inference
Bibtex Type: Talk (talk)

Digital: 0
Event Name: ECML Discovery Challenge Workshop
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Semi-Supervised Support Vector Machines and Application to Spam Filtering},
  author = {Zien, A.},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = sep,
  year = {2006},
  month_numeric = {9}