在机器学习中,表征学习或表示学习是允许系统从原始数据中自动发现特征检测或分类所需的表示的一组技术。这取代了手动特征工程,并允许机器学习特征并使用它们执行特定任务。在有监督的表征学习中,使用标记的输入数据来学习特征,包括监督神经网络,多层感知器和(监督)字典学习。在无监督表征学习中,特征是与未标记的输入数据一起学习的,包括字典学习,独立成分分析,自动编码器,矩阵分解和各种形式的聚类。

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行人再识别(person re-identification, ReID)旨在解决跨摄像头跨场景下目标行人的关联与匹配, 作为智能视频监控系统的关键环节, 对维护社会公共秩序具有重大作用. 为了深入了解行人再识别研究现状和加速推进国内行人再识别相关研究及技术落地, 本文对该领域国家自然科学基金申报数量、资助力度以及地理分布情况进行统计, 并针对近年来发表在国际顶级会议和期刊上的行人再识别研究进行全面梳理. 具体地, 首先阐述一个标准行人再识别算法流程, 并总结其中3个关键技术:表征学习、度量学习和重排序优化. 随后, 列举了实际开放场景中面临的主要难点与挑战, 并据此概括了7种开放行人再识别任务:遮挡、无监督、半监督、跨模态、场景行人搜索、对抗鲁棒和快速检索. 此外, 本文整理了标准行人再识别和开放行人再识别的代表性数据集, 并且对一些代表性行人再识别算法进行比较. 最后本文对行人再识别的未来发展趋势进行展望.

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The rapid mutation of the influenza virus threatens public health. Reassortment among viruses with different hosts can lead to a fatal pandemic. However, it is difficult to detect the original host of the virus during or after an outbreak as influenza viruses can circulate between different species. Therefore, early and rapid detection of the viral host would help reduce the further spread of the virus. We use various machine learning models with features derived from the position-specific scoring matrix (PSSM) and features learned from word embedding and word encoding to infer the origin host of viruses. The results show that the performance of the PSSM-based model reaches the MCC around 95%, and the F1 around 96%. The MCC obtained using the model with word embedding is around 96%, and the F1 is around 97%.

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The rapid mutation of the influenza virus threatens public health. Reassortment among viruses with different hosts can lead to a fatal pandemic. However, it is difficult to detect the original host of the virus during or after an outbreak as influenza viruses can circulate between different species. Therefore, early and rapid detection of the viral host would help reduce the further spread of the virus. We use various machine learning models with features derived from the position-specific scoring matrix (PSSM) and features learned from word embedding and word encoding to infer the origin host of viruses. The results show that the performance of the PSSM-based model reaches the MCC around 95%, and the F1 around 96%. The MCC obtained using the model with word embedding is around 96%, and the F1 is around 97%.

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