2015
Twice-universal piecewise linear regression via infinite depth context trees
ICASSP 2015accepted
We investigate the problem of sequential piecewise linear regression from a competitive framework. For an arbitrary and unknown data length n, we first introduce a method to partition the regressor space. Particularly, we present a recursive method that divides the regressor space into O(n) disjoint…