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Measure Based Regularization


Conference Paper


We address in this paper the question of how the knowledge of the marginal distribution $P(x)$ can be incorporated in a learning algorithm. We suggest three theoretical methods for taking into account this distribution for regularization and provide links to existing graph-based semi-supervised learning algorithms. We also propose practical implementations.

Author(s): Bousquet, O. and Chapelle, O. and Hein, M.
Book Title: Advances in Neural Information Processing Systems 16
Journal: Advances in Neural Information Processing Systems
Pages: 1221-1228
Year: 2004
Month: June
Day: 0
Editors: Thrun, S., L. Saul, B. Sch{\"o}lkopf
Publisher: MIT Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: Seventeenth Annual Conference on Neural Information Processing Systems (NIPS 2003)
Event Place: Vancouver, BC, Canada

Address: Cambridge, MA, USA
Digital: 1
ISBN: 0-262-20152-6
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Measure Based Regularization},
  author = {Bousquet, O. and Chapelle, O. and Hein, M.},
  journal = {Advances in Neural Information Processing Systems},
  booktitle = {Advances in Neural Information Processing Systems 16},
  pages = {1221-1228},
  editors = {Thrun, S., L. Saul, B. Sch{\"o}lkopf},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = jun,
  year = {2004},
  month_numeric = {6}