Relevance feedback algorithm based on support vector machine and rough set for image retrieval is approached.
研究基于支持向量机和粗糙集的相关反馈图像检索算法。
To improve the efficiency of relevance feedback quickly, an integrated memorization and (semi -) supervision active relevance feedback algorithm is presented.
为快速提高相关反馈算法的效率，提出一种记忆与半监督相结合的主动相关反馈算法。
In order to improve the performance of information retrieval system, a retrieval algorithm combining multi-query data fusion with positive relevance feedback is presented.
为提高信息检索系统的性能，提出了一种多查询数据融合与正相关反馈相结合的检索算法。
The algorithm combines active learning, biased classification and incremental learning to model the small sample biased learning problem in relevance feedback process.
该算法将主动式学习、有偏分类和增量学习结合起来，对相关反馈过程中的小样本有偏学习问题进行建模。
A new relevance feedback scheme is proposed based on a support vector learning algorithm for ordinal regression.
基于持向量学习算法，提出了一种新的相关反馈方案。
Meanwhile, the technology of relevance feedback is combined with the presented algorithm to enhance the effectiveness of retrieval.
同时，还将相关反馈技术融合到多分辨率分块主色算法中，以改善检索效果。
Meanwhile, the technology of relevance feedback is combined with the presented algorithm to enhance the effectiveness of retrieval.
同时，还将相关反馈技术融合到多分辨率分块主色算法中，以改善检索效果。
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