@InProceedings{Jaskowski2007evolutionary,
  Title                    = {Evolutionary Learning with Cross-Class Knowledge Reuse for Handwritten Character Recognition},
  Author                   = {Wojciech Jaśkowski and Krzysztof Krawiec and Bartosz Wieloch},
  Booktitle                = {Proceedings of Planning to Learn Workshop, PlanLearn'07},
  Year                     = {2007},
  Editor                   = {Pavel Brazdil and Abraham Bernstain},
  Pages                    = {21--30 },

  Abstract                 = { We propose a learning algorithm that reuses knowledge acquired inpast learning sessions to improve its performance on a new learningtask. The method concerns visual learning and uses genetic programmingto represent hypotheses, each of them being a procedure that processesvisual primitives derived from the training images. The process ofrecognition is generative, i.e., a procedure is supposed to restorethe shape of the processed object by drawing its reproduction ona separate canvas. This basic method is extended with a knowledge reuse mechanism that allows learners to import genetic material from hypotheses that evolved for the other decision classes (object classes).We compare both methods on a task of handwritten character recognition,and conclude that knowledge reuse leads to significant improvementof classification accuracy and reduces the risk of overfitting. },
  Url                      = { http://www.ecmlpkdd2007.org/CD/workshops/PlanLearn/WS_PlanLearn_print.pdf }
}