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1. 发明申请

US20160314411A1 CLASSIFICATION OF HIGHLY-SKEWED DATA 审中-公开
标题翻译：高分辨率数据分类
公开(公告)号：US20160314411A1
公开(公告)日：2016-10-27
申请号：US15137603
申请日：2016-04-25
申请人： Regents of the University of Minnesota
发明人： Vipin Kumar , Varun Mithal , Guruprasad Nayak , Ankush Khandelwal
IPC分类号： G06N99/00
摘要： A method for identifying highly-skewed classes using an imperfect annotation of every instance together with a set of features for all instances. The imperfect annotations designate a plurality of instances as belonging to the target rare class and others to the majority class. First, a classifier is trained on the set of features using the imperfect annotation as supervision, to designate each instance to either the rare class or majority class. A combination of the predictions from the trained classifier and the imperfect annotations is then used to classify each instance to either the rare class or majority class. In particular, an instance is classified to the rare class only when both the trained classifier and the imperfect annotation classify the instance to the rare class. Finally, for each instance assigned as a rare class instance by the combination stage, all instances in its neighborhood are re-classified as either rare class or majority class.
摘要翻译：使用每个实例的不完全注释以及所有实例的一组特征来识别高偏斜类的方法。不完美的注释将多个实例指定为属于目标稀有类，其他实例指向多数类。首先，使用不完美注释作为监督对一组特征对分类器进行训练，将每个实例指定为稀有类或多数类。然后将来自训练分类器和不完美注释的预测的组合用于将每个实例分类到稀有类或多数类。特别地，只有当经过训练的分类器和不完全注释将实例分类到稀有类时，实例才被分类为稀有类。最后，对于通过组合阶段分配为罕见类实例的每个实例，其邻域中的所有实例都被重新分类为稀有类或多数类。

2. 发明申请

US20180130193A1 SIMULTANEOUS ESTIMATION OF LOCATION ELEVATIONS AND WATER LEVELS 审中-公开
公开(公告)号：US20180130193A1
公开(公告)日：2018-05-10
申请号：US15807135
申请日：2017-11-08
申请人： Regents of the University of Minnesota
发明人： Varun Mithal , Ankush Khandelwal , Vipin Kumar
IPC分类号： G06T7/00 , G06K9/62 , G06K9/03 , G06T7/12 , G06T7/162
CPC分类号： G06T7/0002 , G06K9/0063 , G06K9/00657 , G06K9/03 , G06K9/4604 , G06K9/6202 , G06K9/6267 , G06T7/12 , G06T7/162 , G06T7/97 , G06T2207/10032 , G06T2207/30181 , G06T2207/30204
摘要： A method improves automated water body extent determinations using satellite sensor values and includes a processor receiving a time-sequence of land cover labels for a plurality of geographic areas represented by pixels in the satellite sensor values. The processor alternates between ordering the geographic areas based on a water level estimates at each time point in the time sequence such that the order of the geographic areas reflects an estimate of the relative elevations of the geographic areas and updating the water level estimates based on the land cover labels for the geographic areas. A final ordering of the geographic areas and a final water level estimate are used to correct the time-sequence of land cover labels.

3. 发明授权

US10776713B2 Classification of highly-skewed data 有权
公开(公告)号：US10776713B2
公开(公告)日：2020-09-15
申请号：US15137603
申请日：2016-04-25
申请人： Regents of the University of Minnesota
发明人： Vipin Kumar , Varun Mithal , Guruprasad Nayak , Ankush Khandelwal
IPC分类号： G06N99/00 , G06K9/62 , C40B20/00 , A61K31/44 , G06Q20/00 , G06Q10/10 , G06N20/00
摘要： A method for identifying highly-skewed classes using an imperfect annotation of every instance together with a set of features for all instances. The imperfect annotations designate a plurality of instances as belonging to the target rare class and others to the majority class. First, a classifier is trained on the set of features using the imperfect annotation as supervision, to designate each instance to either the rare class or majority class. A combination of the predictions from the trained classifier and the imperfect annotations is then used to classify each instance to either the rare class or majority class. In particular, an instance is classified to the rare class only when both the trained classifier and the imperfect annotation classify the instance to the rare class. Finally, for each instance assigned as a rare class instance by the combination stage, all instances in its neighborhood are re-classified as either rare class or majority class.

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