Intuitively, if the actual value of an input feature isnot needed (i.e., can be replaced with aconstant) toarriveatsimilar (stable) outcomes for most prediction instances, then theuseofthefeature violates thedata minimization principle.

We propose a trajectory planning and control theory for continuous movements such as connected cursive handwriting and continuous natural speech. Its hardware is based on our previously proposed forward-inverse-relaxation neural network (Wada & Kawato, 1993). Computationally, its optimization principle is the minimum torque(cid:173) change criterion. Regarding the representation level, hard constraints satisfied by a trajectory are represented as a set of via-points extracted from a handwritten character. Accordingly, we propose a via-point estimation algorithm that estimates via-points by repeating the trajectory formation of a character and the via-point extraction from the character.