期刊名称：International Journal of Data Mining & Knowledge Management Process
印刷版ISSN：2231-007X
电子版ISSN：2230-9608
出版年度：2017
卷号：7
期号：5/6
页码：63
DOI：10.5121/ijdkp.2017.7605
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Content based retrieval has an advantage of higher prediction accuracy as compared to tagging basedapproach. However, the complexity in its representation and classification approach, results in lowerprocessing accuracy and computation overhead. The correlative nature of the feature data are un-exploredin the conventional modeling, where all the data features are taken as a set of feature values to give adecision. The recurrent feature class attribute is observed for the feature regrouping in action modelprediction. In this paper a co-relative information, bounding grouping approach is suggested for actionmodel prediction in CBMR application. The co-relative recurrent feature mapping results in fasterretrieval process as compared to the conventional retrieval system.
关键词：Recurrent feature mapping; classification; action model prediction; CBMR.
