@inproceedings{3928,
  title = {Personalized handwriting recognition via biased regularization},
  journal = {Proceedings of the 23rd International Conference on Machine Learning (ICML 2006)},
  booktitle = {ICML 2006},
  abstract = {We present a new approach to personalized handwriting recognition.
  The problem, also known as writer adaptation, consists of converting
  a generic (user-independent) recognizer into a personalized
  (user-dependent) one, which has an improved recognition rate for a
  particular user. The adaptation step usually involves user-specific
  samples, which leads to the fundamental question of how to fuse this
  new information with that captured by the generic recognizer. We
  propose adapting the recognizer by minimizing a regularized risk
  functional (a modified SVM) where the prior knowledge from the
  generic recognizer enters through a modified regularization term.
  The result is a simple personalization framework with very good
  practical properties. Experiments on a 100 class real-world data set
  show that the number of errors can be reduced by over 40% with as
  few as five user samples per character.},
  pages = {457-464},
  editors = {Cohen, W. W., A. Moore},
  publisher = {ACM Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {New York, NY, USA},
  month = jun,
  year = {2006},
  slug = {3928},
  author = {Kienzle, W. and Chellapilla, K.},
  month_numeric = {6}
}