城市功能区是认知城市复杂系统的重要单元。然而,由于城市系统的复杂性,城市功能区分类目前仍存在一定的挑战。本文提出构建一种基于POI(Point of Interest,兴趣点)和主动学习算法的城市功能区分类方法。一方面,该方法采用的主动学习算...城市功能区是认知城市复杂系统的重要单元。然而,由于城市系统的复杂性,城市功能区分类目前仍存在一定的挑战。本文提出构建一种基于POI(Point of Interest,兴趣点)和主动学习算法的城市功能区分类方法。一方面,该方法采用的主动学习算法是一种半监督分类方法。相较于非监督分类方法,该方法可具有更高的分类精度;相较于监督分类方法,该方法仅需标记少量的功能区类型,而由于城市系统的复杂性,功能区类型的标记往往需要行业专家的专业知识及对城市深入地熟悉与了解。另一方面,相较于现有研究中常用的大数据(高空间分辨率遥感影像、POI、移动通信、公交刷卡等社会感知数据),该方法选择的POI数据具有易获取、数据完整性高且兼顾城市系统中地理实体的自然属性和社会经济属性的特点,使得本方法具有较高的可行性。本文以北京市朝阳区为例,采用该方法进行城市功能区分类,并将识别结果与人工识别结果进行对比分析,验证了本方法的可行性与准确性,然后分析了该方法实现过程中的两个重要参数对分类结果准确性的影响。展开更多
In speech recognition, acoustic modeling always requires tremendous transcribed samples, and the transcription becomes intensively time-consuming and costly. In order to aid this labor-intensive process, Active Learni...In speech recognition, acoustic modeling always requires tremendous transcribed samples, and the transcription becomes intensively time-consuming and costly. In order to aid this labor-intensive process, Active Learning （AL） is adopted for speech recognition, where only the most informative training samples are selected for manual annotation. In this paper, we propose a novel active learning method for Chinese acoustic modeling, the methods for initial training set selection based on Kullback-Leibler Divergence （KLD） and sample evaluation based on multi-level confusion networks are proposed and adopted in our active learning system, respectively. Our experiments show that our proposed method can achieve satisfying performances.展开更多
文摘城市功能区是认知城市复杂系统的重要单元。然而,由于城市系统的复杂性,城市功能区分类目前仍存在一定的挑战。本文提出构建一种基于POI(Point of Interest,兴趣点)和主动学习算法的城市功能区分类方法。一方面,该方法采用的主动学习算法是一种半监督分类方法。相较于非监督分类方法,该方法可具有更高的分类精度;相较于监督分类方法,该方法仅需标记少量的功能区类型,而由于城市系统的复杂性,功能区类型的标记往往需要行业专家的专业知识及对城市深入地熟悉与了解。另一方面,相较于现有研究中常用的大数据(高空间分辨率遥感影像、POI、移动通信、公交刷卡等社会感知数据),该方法选择的POI数据具有易获取、数据完整性高且兼顾城市系统中地理实体的自然属性和社会经济属性的特点,使得本方法具有较高的可行性。本文以北京市朝阳区为例,采用该方法进行城市功能区分类,并将识别结果与人工识别结果进行对比分析,验证了本方法的可行性与准确性,然后分析了该方法实现过程中的两个重要参数对分类结果准确性的影响。
基金Acknowledgements This study is supported by the National Natural Science Foundation of China （60705019）, the National High-Tech Research and Development Plan of China （ 2006AA010102 and 2007AA01Z417）, the NOKIA project, and the 111 Project of China under Grant No. 1308004.
文摘In speech recognition, acoustic modeling always requires tremendous transcribed samples, and the transcription becomes intensively time-consuming and costly. In order to aid this labor-intensive process, Active Learning （AL） is adopted for speech recognition, where only the most informative training samples are selected for manual annotation. In this paper, we propose a novel active learning method for Chinese acoustic modeling, the methods for initial training set selection based on Kullback-Leibler Divergence （KLD） and sample evaluation based on multi-level confusion networks are proposed and adopted in our active learning system, respectively. Our experiments show that our proposed method can achieve satisfying performances.
