## Abstract

We consider the problem of maintaining the data-structures of a partition-based regression procedure in a setting where the training data arrives sequentially over time. We prove that it is possible to maintain such a structure in time O(log n) at any time step n while achieving a nearly-optimal regression rate of Õ (n-2/(2+d)) in terms of the unknown metric dimension d. Finally we prove a new regression lower-bound which is independent of a given data size, and hence is more appropriate for the streaming setting.

Original language | English (US) |
---|---|

Title of host publication | Advances in Neural Information Processing Systems |

Publisher | Neural information processing systems foundation |

State | Published - Jan 1 2013 |

Externally published | Yes |