9 doan a , domingos p , halevy a . reconciling schemas of disparate data sources : a machine - learning approach . in proc 全局本體存儲一個給定信息領域的所有句法和語義細節(jié)局部模式本體是使用全局本體提取出來的。
The hpso employs local version constriction factor method and global version inertia weight method simultaneously to achieve relatively high performance Hpso同時采用局部模式的壓縮因子方法和全局模式的慣性權(quán)重方法以獲得相對較高的性能。
It is therefore recommended to exploit the current path and current schema special registers instead of qualifying the local schema expclicitly when defining sql objects 中指定其他模式,建議在定義sql對象時利用特殊寄存器current path和current schema ,而不是顯式地指定局部模式。
The mapping between global schema , export schema and local schema is presented based on the model , which solves the problem of mapping between jidm and relational data , xml files , object - oriented data model 在該模型的基礎上,介紹了全局模式、輸出模式以及局部模式之間的映射關系,解決了jidm模型與關系模型、 xml文件以及面向?qū)ο竽P椭g的映射問題。
An attention - based image recognition model is proposed . when analyze complex visual field or pattern , visual attention mechanism is used to detect saliency features in the image and drive the fixation point to scan the saliency features sequentially . during each fixation , the local pattern at the fixation point is memorized or matched . there are two parts in the memory of a complex pattern , the memory of local patterns that constitute the complex pattern and the memory of space relations between local patterns . corresponding to memory process , the recognition process also contains two parts , the matching of local patterns and the matching of space relations between local patterns . an object is recognized only when there are enough numbers of local patterns is matched and the space relations between these local patterns are correct 在掃描到每一個關鍵特征區(qū)時,將對該區(qū)域附近的局部模式進行記憶或匹配。對一個復雜目標的記憶將包括2部分,一部分是對局部模式的記憶,即組成該目標的各“部件”的模式另一部分是對各局部模式之間的空間關系的記憶,即組成該目標的各“部件”之間的結(jié)構(gòu)關系。與記憶過程對應,識別過程也包括2部分,一部分是對局部模式的匹配,另一部分是對各局部模式之間結(jié)構(gòu)關系的匹配。