您要查找的是不是：
- The file referenced on the FILE subcommand does not have the proper format for QUICK CLUSTER initial cluster centers. 究竟初始类中心的文件要如何格式化书写啊？敬请各位大侠高手指教。不胜感激！
- Based on the initial clusters center generated by the LBG in every speakers feature space,every sample can be classified,so every clusters area can be parallel covered by the PONN. 该方法以LBG算法生成每个说话人特征空间初始的聚类中心,对本类样本按聚类中心分类后,用前馈优先级神经网络(PONN)对每个聚类区进行并行覆盖。
- A Cell Density-Enabled Schema for Initializing Cluster Centers 融合网格密度的聚类中心初始化方案
- Initial clustering centers 初始聚类中心
- Initial cluster centers 初始聚类中心
- initial clustering center 初始聚类中心
- initial cluster center 初始类心
- The initial clustering of embryonic cells from which a part or an organ develops; primordium. 原基，芽基，胚基长出部位或器官的胚胎细胞群；原基
- The initial clustering of embryonic cells from which a part or an organ develops;primordium. 原基，芽基，胚基长出部位或器官的胚胎细胞群;原基
- An error occurred attempting to initialize cluster formation. 试图初始化群集结构时出错。
- It is proved that if the cost function is not zero after the algorithm converges, then all hidden units will move to the weighted cluster centers of sample inputs. 主要结论包括：如果算法收敛后损失函数不为零，则各隐节点将位于样本输入的加权聚类中心；
- The five bright white points near the cluster center are actually images of a single distant quasar. 在星团中心的这五颗明亮的白点实际上是一个单一遥远类星体的图像。
- A novel method for extracting discriminant features by using kernel methods, kernel optimal transformation and cluster centers algorithm (KOT-CC), is presented. 基于统计学习理论的核化原理，提出一种新的鉴别特征提取方法-核最优变换与聚类中心算法。
- A problem occurred when the wizard attempted to initialize cluster creation. 向导试图初始化群集创建时发生错误。
- The cluster centers will then be used as a basis to define a Fuzzy Inference System (FIS) which can then be used to explore and understand traffic patterns. 群中心然后将被用于得为据定义可能然后被用于探索和了解交通图的一个模糊的推理系统(FIS)。
- K-mean clustering algorithm is used to get the original training set,the clustering center is considered to be the original training set,and so reduces the redundance of samples. 该算法利用K均值聚类算法对训练样本集进行压缩,将聚类中心作为初始训练样本集,减少了样本间的冗余,提高了学习速度。
- The BT -SVM muti - classification is formed by learning sample classes and the optimal hyperplanes which are constructed in the nodes of binary tree by the samples corresponding to clustering centers. 该方法在二叉树各节点处根据聚类中心所对应的样本构造学习样本集和最优分类超平面,保障了聚类精度,有效地提高了测试正确率。
- FCM is used to cluster image.In image retrieval，the querying image is compared to cluster center，then retrieved in the class with the minimal distance. 采用模糊C均值算法对图像进行聚类，在图像检索时，查询图像和聚类中心比较，然后在距离最小的类中进行检索。
- Experiment is performed on a L-band NASA/JPL SIR-C polarimetric SAR image over Danshui town, Guangdong, P.R.China.Furthermore, the movements of the clustering centers are discussed. 以中国广东淡水附近的L波段NASA/JPL SIR-C全极化SAR图像作为实验数据进行了仿真试验，并进一步对聚类中心的迁移进行了讨论。
- The sum of the Euclidean distances of the points from their respective cluster centers is adopted as the similarity metric. The near optimal cluster centers are chosen by the genetic algorithm. 对聚类中心采用二进制编码,把每一个点到它们各自的聚类中心的欧几里得距离的总和作为相似度量,通过遗传算法寻找聚类中心。
