Evolutionary Learning with Cross-Class Knowledge Reuse for Handwritten Character Recognition
Wojciech Ja±kowski, Krzysztof Krawiec, Bartosz Wieloch · 2007
We propose a learning algorithm that reuses knowledge acquired in past learning sessions to improve its performance on a new learning task. The method concerns visual learning and uses genetic programming to represent hypotheses, each of them being a procedure that processes visual primitives derived from the training images. The process of recognition is generative, i.e., a procedure is supposed to restore the shape of the processed object by drawing its reproduction on a 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 signi cant improvement of classi cation accuracy and reduces the risk of over tting.