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Using the learned representations of ambiguous instances, we further adapt rival penalization competitive learning to conduct instances based word sense clustering, allowing us to determine the number of word senses automatically.
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In this framework, many existing ranking algorithms such as the simple distance-based ranking method can be directly adopted for sorting all 3D models in the database using the learned new feature representation and returning the top ranked models to the user.
Therefore, a 3-D FLC can be established using the learned results of a SVR.
Next, the results are obtained when E F (d) is defined using the learned features of both first and second layers.
Using the learned pattern from this training set, age was predicted on the temporary test set.
Second, the bnc module makes predictions on new examples using the learned network and CPDs.
Next, we describe how new networks are constructed using the learned functions.
Learning in SSS is performed using these labels, and the edges of the network are weighted using the learned models.
We evaluated detection time and spatial accuracy using the learned graphs for these simulated injects (Fig. 1).
The effectiveness of our approach is shown by the good generalization of the learned representations with new six public datasets.
Based on the learned representations through multiple hidden layers, a representation clustering algorithm is proposed to minimize the distance of intra-class variations and maximize the distance of inter-class variations simultaneously.
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