Learning One More Thing

Abstract

Most research on machine learning has focused on scenarios in which a learner faces a single isolated learning task. The lifelong learning framework assume, that the learner encounters a multitude of related learning tasks over Us lifetime providing the opportunity for the transfer of knowledge among these. This paper studies lifelong learning in the context of binary classification. It presents the invariance approach in which knowledge is transferred via a learned model of the invariances of the domain Results on learning to recognize objects from color images demonstrate superior generalization capabilities of invariances are learned and used to bias subsequent learning.