A scalable method for classifier knowledge reuse

Just as a person´s life-long experience helps him/her in novel tasks, it would be useful to leverage the knowledge in previously trained classifiers in learning future classification tasks that may be related. We present a maximum posterior probability method for classifier knowledge reuse that is novel in its scalability with the quantity of classifiers reused and in its ability to incorporate different classifier architectures. Also, we describe a mutual information based relevance criterion to identify previously trained classifiers that may help in the current task. Results from application of this method and criterion to public domain data sets demonstrate their usefulness in improving classifier performance, speeding up learning, and assisting in problem decomposition