The probability result for each SVM is smoothed using a morphological sieve approach [93], this has the effect of smoothing out short spikes/drops in the classification signal by averaging the results temporally over a window of frames, similar to the approach used in Section 3.
Sample text:```The merging is based on the increase in the weighted-bycounts entropy [1] and is stopped by a threshold indicating the minimum number of training samples required to estimate a weight distribution.```
Sample intent: method
Sample text:```This seems to be the main drawback of the current implementation in comparison to the method of Black Jepson (“refined” in Table 1).```
Sample intent: result



Sample text:```…between output and input, CNF achieves almost 10% relative improvement over CRF. CNF also outperforms other methods including SVMpro (Hua and Sun, 2001), SVMpsi (Kim and Park, 2003), YASSPP (Karypis, 2006), PSIPRED (Jones, 1999), SPINE (Dor and Zhou, 2007) and TreeCRFpsi (Dietterich et al., 2004).```
Sample intent: background



Sample text:```Discrepancies analogous to those between our results and those of simulations have, indeed, recently been observed in the case of the binary perceptron [18], the pspin interaction spin glass [19], and in the case of fluctuating manifolds in random media [20], where dynamically frozen phases were observed in regions of parameter space in which static approaches yielded ergodic phases.```
Sample intent: result



Sample text:```13 indicates the comparison of the results of the mesh simplification method [2] after applying different mesh enhancement techniques.```
Sample intent: background



Sample text:```In-between methods can be subdivided into linear approximations [12,24] and quadratic interpolation methods [22,23,25–27] based on the degree of the polynomial approximation employed.```
Sample intent: method
Sample text:```…methods rely on some additional and often heuristic step to generate an initial solution, such as adversarially-trained neural networks [Conneau et al., 2018, Zhang et al., 2017a,b], robust self-learning [Artetxe et al., 2018b] or
5This can be easily verified by noting that the…```
Sample intent: background



Sample text:```The next communication [2] considered the most general characteristics of the parameter of spa tial EEG synchronization (the average in each of the fre quency bands of the EEG’s coherence value), such as multiplicity, directedness, and reproducibility of varia tions of this parameter owing to the change of state of the subjects.```
Sample intent: method



Sample text:```In this study, we generalized previous results regarding the quasi-Gaussian shape and the scalar property using the SBF model [13,21,52].```
Sample intent: result



Sample text:```In-between methods can be subdivided into linear approximations [12,24] and quadratic interpolation methods [22,23,25–27] based on the degree of the polynomial approximation employed.```
Sample intent: method



Sample text:```Discrepancies analogous to those between our results and those of simulations have, indeed, recently been observed in the case of the binary perceptron [18], the pspin interaction spin glass [19], and in the case of fluctuating manifolds in random media [20], where dynamically frozen phases were observed in regions of parameter space in which static approaches yielded ergodic phases.```
Sample intent: result



Sample text:```For seeking the optimal value of Wh of the sub-problems which corresponds to the h-th level, we can solve the problem by the smoothing proximal gradient (SPG) method [6] fortunately.```
Sample intent: background



Sample text:```The merging is based on the increase in the weighted-bycounts entropy [1] and is stopped by a threshold indicating the minimum number of training samples required to estimate a weight distribution.```
Sample intent: method



Sample text:```13 indicates the comparison of the results of the mesh simplification method [2] after applying different mesh enhancement techniques.```
Sample intent: background



Sample text:```…between output and input, CNF achieves almost 10% relative improvement over CRF. CNF also outperforms other methods including SVMpro (Hua and Sun, 2001), SVMpsi (Kim and Park, 2003), YASSPP (Karypis, 2006), PSIPRED (Jones, 1999), SPINE (Dor and Zhou, 2007) and TreeCRFpsi (Dietterich et al., 2004).```
Sample intent: background



Sample text:```Over the last few years, many different local interest point detectors (like Harris3D [16], Cuboid [6], Hessian [35] or Dense sampling [33]) and many spatio-temporal descriptors (like HOG [17], HOG3D [14], HOF [17], Cuboid [6] or ESURF [35]) have been proposed.```
Sample intent: method