Sentence examples for design a dictionary from inspiring English sources

Definition 2 (sentiment dictionary D): for each sentiment, we can design a dictionary which can represent it sharply, called sentiment dictionary.

We consider a dictionary learning problem aimed at designing a dictionary such that the signals admit a sparse or an approximate sparse representation over the learnt dictionary.

Since the matching pursuit is a greedy algorithm to find RF and gradient waveforms which are the best match for an object-signal, the signal can be decomposed with a few iterations and thereby lead reduction of imaging time in MR. To adopt the matching pursuit algorithm to the adaptive data acquisition in MRI, we have designed a dictionary which contains a windowed Fourier basis set.

In[3, 4], the authors introduce a nonsubsampled shearlet transform to design a separation dictionary which greatly increase the redundancy.

Furthermore, we design an effective dictionary updating mechanism.

Since designing a pronunciation dictionary requires language-specific expertise, the need for manual supervision was assessed by comparing phonemic and graphemic units for acoustic modeling.

We take the unique and similar components of different speakers׳ signals into account, and design a new structured dictionary which contains discriminative and buffer sub-dictionaries.

Further, a new discriminative dictionary learning algorithm is designed for learning a dictionary from training samples to enhance the discriminative capability of its coding vectors.

A two-level strategy could be designed: in each region, a dictionary of templates of incompatible folding patterns would be collected and matched in a way or another using rare architectural information, while individual subjects would be aligned using diffeomorphisms to the closest template.

The proposed design has been validated with a dictionary of more than 145 000 accepted meanings.

We design a strategy based on a dictionary of graph edit operations to automatically identify and correct the errors in the input graph.