Adaptive On-line Learning in Changing Environments

Noboru Murata, Klaus-Robert Müller, Andreas Ziehe, Shun-ichi Amari

Abstract: An adaptive on-line algorithm extending the learning of learning idea is proposed and theoretically motivated. Relying only on gra(cid:173) dient flow information it can be applied to learning continuous functions or distributions, even when no explicit loss function is gi(cid:173) ven and the Hessian is not available. Its efficiency is demonstrated for a non-stationary blind separation task of acoustic signals.