With the goal to generate more scalable algorithms with higher efficiency and fewer open parameters, reinforcement learning (RL) has recently moved towards combining classical tec...
In kernel-based regression learning, optimizing each kernel individually is useful when the data density, curvature of regression surfaces (or decision boundaries) or magnitude of...
—This paper reports the design and development processes of a totally decoupled flexure-based XYZ parallel-kinematics micropositioning stage with piezoelectric actuation. The un...
We examine the so-called rigorous support vector machine (RSVM) approach proposed by Vapnik (1998). The formulation of RSVM is derived by explicitly implementing the structural ris...
We show how to apply learning methods to two robotics problems, namely the optimization of the on-board controller of an omnidirectional robot, and the derivation of a model of the...