随着互联网经济的迅猛发展,PO(IPoint Of Interest)搜索成为空间信息服务业发展的核心技术之一。提高用户满意度无疑是POI搜索引擎的最终目标。通过挖掘用户访问日志,建立反馈相似度模型,可提高搜索结果准确度,优化POI搜索引擎。通过理...随着互联网经济的迅猛发展,PO(IPoint Of Interest)搜索成为空间信息服务业发展的核心技术之一。提高用户满意度无疑是POI搜索引擎的最终目标。通过挖掘用户访问日志,建立反馈相似度模型,可提高搜索结果准确度,优化POI搜索引擎。通过理论分析,该方法在不增加计算时间的基础上提高了搜索结果的准确性。最后将该方法应用于中国科学院计算技术研究所地理信息中心自主研发的通图(www.tongmap.cn)地图搜索引擎中,结合实际数据测试,说明该方法在优化POI搜索引擎方面是行之有效的。展开更多
This paper describes a new method for active learning in content-based image retrieval. The proposed method firstly uses support vector machine (SVM) classifiers to learn an initial query concept. Then the proposed ac...This paper describes a new method for active learning in content-based image retrieval. The proposed method firstly uses support vector machine (SVM) classifiers to learn an initial query concept. Then the proposed active learning scheme employs similarity measure to check the current version space and selects images with maximum expected information gain to solicit user's label. Finally, the learned query is refined based on the user's further feedback. With the combination of SVM classifier and similarity measure, the proposed method can alleviate model bias existing in each of them. Our experiments on several query concepts show that the proposed method can learn the user's query concept quickly and effectively only with several iterations.展开更多
文摘随着互联网经济的迅猛发展,PO(IPoint Of Interest)搜索成为空间信息服务业发展的核心技术之一。提高用户满意度无疑是POI搜索引擎的最终目标。通过挖掘用户访问日志,建立反馈相似度模型,可提高搜索结果准确度,优化POI搜索引擎。通过理论分析,该方法在不增加计算时间的基础上提高了搜索结果的准确性。最后将该方法应用于中国科学院计算技术研究所地理信息中心自主研发的通图(www.tongmap.cn)地图搜索引擎中,结合实际数据测试,说明该方法在优化POI搜索引擎方面是行之有效的。
文摘This paper describes a new method for active learning in content-based image retrieval. The proposed method firstly uses support vector machine (SVM) classifiers to learn an initial query concept. Then the proposed active learning scheme employs similarity measure to check the current version space and selects images with maximum expected information gain to solicit user's label. Finally, the learned query is refined based on the user's further feedback. With the combination of SVM classifier and similarity measure, the proposed method can alleviate model bias existing in each of them. Our experiments on several query concepts show that the proposed method can learn the user's query concept quickly and effectively only with several iterations.
