As described in the methods we experimented with various combinations of features, building 4 different SVMs.
To demonstrate the performance of the proposed method, we perform experiments on various databases.
In order to demonstrate the performance of the proposed method, we conducted experiments on publicly available datasets.
To prove the effectiveness of the proposed method, we perform experiments using a humanoid robot, the HRP-4 [7].
To validate the method, we report experiments in unknown, non-predictable, unstructured, cluttered, dense and complex environments.
To validate the method, we conducted experiments with an actual tracked vehicle on our simulated stairs, and we observed some divergences between our calculation and the experimental result.
In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature.
Employing the Taguchi method, we arranged experiments and assessed the applicability of the three kinds of indexes regarding tractor Phase I overturn.
Based on a factorial design method, we conducted experiments with different combinations of the two factors to study their effects on the process output (i.e. density of the nanowires), which was evaluated by a scanning electron microscope (SEM).
To evaluate the method, we run experiments on a benchmark set proposed by Sandve et al. (2007).
