On-line Learning in Switching and Drifting Environments with Application to Blind Source Separation
Klaus‐Robert Müller, Andreas Ziehe, Noboru Murata, Шун-ичи Амари · Cambridge University Press eBooks · 1999
An adaptive on-line algorithm extending the learning of learning idea is proposed and theoretically motivated. Relying only on gradient flow information it can be applied to learning continuous functions or distributions, even when no explicit loss function is given and the Hessian is not available. Its efficiency is demonstrated for drifting and switching non-stationary blind separation tasks of accoustic signals. Introduction Neural networks are powerful tools that can capture the structure in data by learning. Often the batch learning paradigm is assumed, where the learner is given all training examples simultaneously and allowed to use them as often as desired. In large practical applications batch learning is experienced to be rather infeasible and instead on-line learning is employed. In the on-line learning scenario only one example is given at a time and then discarded after learning. So it is less memory consuming and at the same time it fits well into more natural learning, where the learner receives new information at every moment and should adapt to it, without having a large memory for storing old data. Appart from easier feasibility and data handling the most important advantage of on-line learning is its ability to adapt to changing environments, a quite common scenario in industrial applications where the data distribution changes gradually over time (e.g. due to wear and tear of the machines). If the learning machine does not detect and follow the change it is impossible to learn the data properly and large generalization errors will result.