公开(公告)号：US20120310864A1

公开(公告)日：2012-12-06

申请号：US13484696

申请日：2012-05-31

申请人： Shayok Chakraborty ,  Vineeth Nallure Balasubramanian ,  Sethuraman Panchanathan

发明人： Shayok Chakraborty ,  Vineeth Nallure Balasubramanian ,  Sethuraman Panchanathan

IPC分类号： G06F15/18

CPC分类号： G06K9/6262 ,  G06N20/00

摘要： This disclosure includes various embodiments of apparatuses, systems, and methods for adaptive batch mode active learning for evolving a classifier. A corpus of unlabeled data elements to be classified is received, a batch size is determined based on a score function, a batch of unlabeled data elements having the determined batch size is selected from the corpus and labeled using a labeling agent or oracle, a classifier is retrained with the labeled data elements, these steps are repeated until a stop criterion has been met, for example, the classifier obtains a desired performance on unlabeled data elements in the corpus. The batch size determination and selection of a batch unlabeled data elements may be based on a single score function. The data elements may be video, image, audio, web text, and/or other data elements.

摘要翻译： 本公开包括用于进化分类器的自适应批模式主动学习的装置，系统和方法的各种实施例。 收到要分类的未标记数据元素的语料库，基于分数函数确定批量大小，从语料库中选择一批具有确定的批量大小的未标记的数据元素，并使用标签代理或分类器 用标记的数据元素重新训练，重复这些步骤，直到满足停止标准为止，例如，分类器在语料库中的未标记的数据元素上获得期望的性能。 批量未标记数据元素的批量大小确定和选择可以基于单分数函数。 数据元素可以是视频，图像，音频，web文本和/或其他数据元素。