ID 原文 译文
47696 实验证明了算法的准确性和高效性。 The experiment results show the ac-curacy and efficiency of proposed algorithm.
47697 为了建立用户精准兴趣模型以有效发现具有相似兴趣的用户群，提出了一种针对微博的短文本特征计算方法用于聚类算法，提升聚类效果以更好地挖掘微博用户的相似兴趣集合。 In order to model the accurate interest preference of microblog users and discover user groups with similar in-terest, a new method was proposed which considered the total amount of retweets, comments and attitudes of each mi-croblog for text feature calculation with utilizing classic analytical hierarchy process method.
47698 该方法融合了微博转发数、评论数、点赞数等多个关键指标来度量微博短文本特征的重要性。 The proposed method usedthree indicators to evaluate the importance of the text feature representation and made an improvement on traditionaltf-idf feature calculation method to fit for short text.
47699 同时，引入层次分析技术，改进了传统的 tf-idf 特征计算方法，并利用经典文本聚类算法进行实验。 Furthermore, this method was also implemented in the traditionalclustering algorithm.
47700 实验结果表明，改进后的短文本特征计算方法与传统的 tf-idf 特征计算方法相比，在类内集中度和类间分散度上取得了更好的效果。 Experimental results show that, compared with the traditional tf-idf method, the improved approachhas a better clustering effect on the average scattering for clusters and the total separation between clusters.
47701 针对 Alpha 稳定分布噪声环境下参数估计性能退化的问题，受类相关熵概念的启发，提出分数低阶类相关熵（FCAS）的概念， According to the performance degradation problem of parameter estimation algorithm in the Alpha stable dis-tribution noise, inspired by the concept of correntropy, a new class of statistics, namely, the fractional lower-order cor-rentropy-analogous statistics (FCAS) was proposed.
47702 并采用分数低阶类相关熵准则对平行因子分析(PARAFAC)算法中基于三线性最小二乘（TALS）迭代准则的目标函数进行了修正，推导出适用于冲激噪声环境的韧性平行因子分析（FCAS-PARAFAC）算法， By employing the fractional lower-order correntropy-analogous sta-tistics based cost function in parallel factor (PARAFAC), the FCAS-PARAFAC algorithm was deduced which can be uti-lized for the parallel factor under impulsive noise environments.
47703 并将该方法应用于双基地 MIMO 雷达系统中目标参数估计中。 The FCAS-PARAFAC algorithm was applied to para-meter estimation in bistatic MIMO radar under impulsive noise environment.
47704 FCAS-PARAFAC 算法能够抑制脉冲噪声的影响，具有较好的估计性能， The proposed method can suppress the im-pulse noise interference and has better estimation performance.
47705 并且能够实现自动配对， Furthermore, the estimated parameters are automaticallypaired without the additional pairing method.