ID 原文 译文
25685 仿真结果验证了 BC-SSAF 和 VＲ-BC-SSAF 性能的优越性。 Simulation results verify the superiority of the BC-SSAF and VＲ-BC-SSAF．
25686 基于短期出租车轨迹数据的载客区域推荐能极大减少系统开销，提高推荐效率，但常伴随着数据稀疏性的问题。 Taxi pick-up areas recommendation based on the short-term taxi trajectory data can greatly reduce the system overhead and improve the efficiency of the recommendation，but it often has the problems of data sparseness．
25687 针对该问题，本文提出了一种融合地理信息的隐语义模型-GeoLFM。 For this reason, a Latent Factor Model integrated with the geographic information, called GeoLFM, is put forward．
25688 该模型通过将出租车司机所处的客观地理环境信息，融合到司机-载客区域矩阵分解的过程中，从而弥补数据稀疏性带来的不足。 This model makes up the faultiness of data sparseness by integrating the geographic information relating to drivers into the Matrix decomposition, which records the visiting relationship between drivers and pick-up areas．
25689 同时，根据出租车实时的空间位置信息，为身处不同地点的出租车推荐不同的载客区域。 Meanwhile, different pick-up areas can be recommended for the taxis in various locations according to the real-time spatial context of the taxis．
25690 实验证明，本文提出的方法与常用方法相比，推荐结果与真实的出租车司机载客情况间的平均绝对误差和均方根误差都得到大幅降低，较好的提升了推荐效果。 Experimental results show that, with the comparison between our proposed method and others, the Mean Absolute Error (MAE) and the Ｒoot MeanSquare Error (ＲMSE) between the recommended results and the actual value are significantly reduced, which indicates the recommendation effect is better improved．
25691 文本序列的自动标注能够解决深度学习普遍面临的人工标注成本过高的问题。 Automatic annotation of text sequence can address the common issue of high manual annotation labor cost in deep learning．
25692 本文针对地址信息的实体表述特征，构建基于实体边界矩阵(Entity Boundary Matrix，EBM)的表示模型，在此基础上提出了一种基于深度学习和 KNN 标签修正算法(K-Nearest Neighbours Correction Algorithm，KNN-CA)的不需要任何人工标注训练集的自动标注算法。 In this paper, a representation model based on the entity boundary matrix (EBM) is constructed. On the basis, we propose an automatic annotation algorithm combining deep learning with KNN annotation correction algorithm (KNN-CA) where the manual labeling training set is not required．
25693 首先获取预置小区数据集并构建离线特征库和初始化在线特征库; Firstly, the offline feature library and online feature library is built and initialized respectively with the utilization of collecting estate dataset．
25694 接着通过匹配算法求解 EBM 并利用 KNN-CA 进行优化，再通过数据增广得到自动标注的训练集; In addition, EBM is solved by matching algorithm and optimized via KNN-CA technique． After the data augmentation process, a training dataset of automaticannotation is obtained．