Virtually all methods of learning dynamic systems from data start from the same basic assumption: the learning algorithm will be given a sequence of data generated from the dynami...
An approach is presented for imposing generic hard constraints on deformable models at a low computational cost, while preserving the good convergence properties of snake-like mod...
We propose a new semi-supervised model selection method that is derived by applying the structural risk minimization principle to a recent semi-supervised generalization error bou...
Virtually all methods of learning dynamic systems from data start from the same basic assumption: that the learning algorithm will be provided with a sequence, or trajectory, of d...
A hybrid learning neuro-fuzzy system with asymmetric fuzzy sets (HLNFS-A) is proposed in this paper. The learning methods of random optimization (RO) and least square estimation (...