1) optimal query vector
最优查询向量
1.
When determining the optimal query vector, the algorithm took the former memorized optimal query vector as the decaying vector, and used the feedback samples which the user selected to determine the decaying factor dynamically.
在确定最优查询向量时,以记忆的前次最优查询向量作为以往正反馈的衰减向量,利用当次用户所选反馈样本动态地确定衰减因子,最后将衰减向量与本次用户所选反馈样本结合不断更新最优查询向量,为后续反馈积累了更多用户检索意图信息。
2) most efficient queries
最优查询
1.
Multi-domain deep Web crawler based on most efficient queries
基于最优查询的多领域deep Web爬虫
3) Query vector
查询向量
5) reverse nearest neighbor queries
反向最近邻查询
1.
The algorithm efficiently solves reverse nearest neighbor queries for continuously moving points in the plane.
为研究动态环境下解决反向最近邻查询的算法,采用TPR-树索引结构给出了解决动态环境下的最近邻查询算法,并提出反向最近邻查询算法。
6) reverse nearest neighbor query
反向最近邻查询
1.
The reverse nearest neighbor query of the moving points
移动点的反向最近邻查询
2.
One of the most important algorithms in spatial database is reverse nearest neighbor query.
反向最近邻查询是空间数据库中最重要的算法之一。
补充资料：查询
1.查问;调查。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条