  We consider object recognition in the context of lifelong learning, where a
robotic agent learns to discriminate between a growing number of object classes
as it accumulates experience about the environment. We propose an incremental
variant of the Regularized Least Squares for Classification (RLSC) algorithm,
and exploit its structure to seamlessly add new classes to the learned model.
The presented algorithm addresses the problem of having an unbalanced
proportion of training examples per class, which occurs when new objects are
presented to the system for the first time.
  We evaluate our algorithm on both a machine learning benchmark dataset and
two challenging object recognition tasks in a robotic setting. Empirical
evidence shows that our approach achieves comparable or higher classification
performance than its batch counterpart when classes are unbalanced, while being
significantly faster.
