@inproceedings{4160,
  title = {A Linear Programming Approach for Molecular QSAR analysis},
  journal = {Proceedings of the International Workshop on Mining and Learning with Graphs 2006 (MLG 2006)},
  booktitle = {MLG 2006},
  abstract = {Small molecules in chemistry can be represented as graphs.
  In a quantitative structure-activity relationship (QSAR) analysis, the
  central task is to find a regression function that predicts
  the activity of the molecule in high accuracy.
  Setting a QSAR as a primal target, we propose a new linear
  programming approach to the graph-based regression problem.
  Our method extends the graph classification algorithm by Kudo et al.
  (NIPS 2004), which is a combination of boosting and graph mining.
  Instead of sequential multiplicative updates, we employ the linear
  programming boosting (LP) for regression. The LP approach allows to
  include inequality constraints for the parameter vector, which turns out to
  be particularly useful in QSAR tasks where activity values are
  sometimes unavailable.
  Furthermore, the efficiency is improved significantly by employing
  multiple pricing.},
  pages = {85-96},
  editors = {G{\"a}rtner, T. , G. C. Garriga, T. Meinl},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = sep,
  year = {2006},
  note = {Best Paper Award},
  author = {Saigo, H. and Kadowaki, T. and Tsuda, K.},
  month_numeric = {9}
}