Sentence examples for feature to solve from inspiring English sources

The experiments performed on both of them suggest that diversification is a key feature to solve the problem.

In this paper, we advocate that the way forward is to integrate SDN and fully utilize its feature to solve the problem.

To this end, first a novel spatial-temporal multi-feature is proposed by joining super-pixel based multi-appearance features and multi-motion features to solve fast moving crowd counting.

This paper proposes an image retrieval using fused deep convolutional features to solve the semantic gap between low-level features and high-level semantic features of traditional contend-based image retrieval method.

In this paper, we propose a learning framework using static, dynamic and sequential mixed features to solve three fundamental problems: spatial domain variation, temporal domain polytrope, and intra- and inter-class diversities.

In this paper, a two-phase approach has been used to determine a reduced set of relevant and non-redundant features to solve the above-mentioned issues.

First, to the best of our knowledge, this is the first time that node identifiers in the egocentric networks are used as features to solve network-based classification problem.

The selection procedure to determine the best set of features to solve the classification problem at hand is included in section 4. The low-cost classifier proposed in the previous section provides an estimation of the IBM minimizing the MSE.

In this paper, we constructed a new heuristic algorithm based on the tabu search and adaptive large neighborhood search (ALNS) with several specifically designed operators and features to solve the capacitated vehicle routing problem (CVRP).

A potential solution is that the rats with hippocampal lesions could make use of individual features to solve the unidirectional place task and the contextual biconditional problem (since there were no common cues to disambiguate), but failed when required to apply configural learning to situations involving multiple, common cues differentiated by their locations.

This method has similarities to TTI [36] as it uses a feature translator to solve the differing feature spaces and also uses co-occurrence data as the link.