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Our method first trains these cascaded networks from labeled image patches.
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In this method, we first train the syllable model and predict syllable durations for a given input of context labels.
Using this method we first train the model using the data at the first time (τ1) point just as above.
The method first transforms multiple spike-trains by convolution with exponentially decaying kernels [19], [20] (low-pass filtering).
The proposed method first performs the clustering of training HR patches and generates two nonlinear eigenspaces of HR patches and their corresponding low-frequency components belonging to each cluster by the KPCA.
In order to obtain accurate HR images, the proposed method first performs clustering of the training HR patches and derives an inverse map for estimating the missing high-frequency components from the two nonlinear eigenspaces of training HR patches and their corresponding low-frequency components in each cluster.
More specifically, the proposed method first adopts several different features to train individual base metric of each feature using a linear KISSME and a non-linear KLFDA and then adopts two optimization approaches, i.e., relative distance-based approach and top recognition at rank-k, to learn weights of the base metrics.
The first trains?
Try the natural method first.
Practice the Julienne method first.
The trip across a corner of Pennsylvania — intended for its marketing appeal, as much as its method of transportation — marked the first train trip of the 2008 race.
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