Abstract— We present a machine learning approach for trajectory inverse kinematics: given a trajectory in workspace, to find a feasible trajectory in angle space. The method lea...
—We present a meta-learning framework for the design of potential functions for Conditional Random Fields. The design of both node potential and edge potential is formulated as a...
It has been observed that traditional decision trees produce poor probability estimates. In many applications, however, a probability estimation tree (PET) with accurate probabilit...
This paper addresses the crucial issue in the design of a proof development system of how to deal with partial functions and the related question of how to treat undefined terms. ...
Preferences in constraint problems are common but significant in many real world applications. In this paper, we extend our conditional and composite CSP (CCCSP) framework, managi...