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k
- 中括号内的序号为寻找聚类中心的迭代运算的次序号。聚类中心的向量值可任意设定，例如可选开始的K个模式样本的向量值作为初始聚类中心。-The number in brackets in order to find the cluster centers iteration sequence number. Cluster center value can be set to, for example, the beginning of an optional mode of samples to t
K--B-value-calculation-function
- 测量K,B值，通过空打粒子数，实测粒子数和标准质量厚度计算KB值-K, B value calculation function
k-epso
- best soudo code for k-epso easy to understand and very useful
K-average-clustering-algorithm
- K-均值聚类算法研究。K - average clustering algorithm research.-K- average clustering algorithm research.
k-means
- k均值算法的ppt，里面包含了模糊c均值算法，有益于对该算法的理解，有较大的帮助-k-means algorithm ppt, which contains a fuzzy c-means algorithm, the benefits of this algorithm understanding, a great help
K-means-CPP
- 通过对K-均值算法的编程实现，加强对该算法的理解和认识。提高自身的知识水平和编程能力，认识模式识别在生活中的应用-Through the K-means algorithm programming to enhance understanding and knowledge of the algorithm. Improve their level of knowledge and programming capabilities, understanding of pattern recogn
Microprocessor-Notes-by-K-Udaya-Kumar
- Microprocessor Notes by K Udaya Kumar VITORY
k-means
- 基于云计算平台Hadoop的并行k-means聚类算法设计研究-Parallel k-means clustering algorithm designed Hadoop cloud-based computing platform
k-means-
- 基于云计算平台Hadoop的并行k-means聚类算法设计研究-Parallel k-means clustering algorithm designed Hadoop cloud-based computing platform
K-nearest-neighbors
- K MEAN CLUSTERING FOR IMAGE PROCESSING
K-nearest-neighbor-algorithm
- 从K近邻算法、距离度量谈到KD树、SIFT+BBF算法,讲解详细，非常有用-From K neighbor algorithm and distance measurement when it comes to KD tree, SIFT+ BBF algorithm, explain in detail, very useful
X-means-Extending-K-means
- X-means k-means扩展 论文 聚类-X-means Extending of k-means cluster
K-means-C
- K-means实现数据聚类，其中包括数据维度，数据分类数-K-means clustering data, including data dimensions, data classification number, etc.
Improved-k-t-BLAST-and-k-t-SENSE-using
- Improved k-t BLAST and k-t SENSE using Improved k-t BLAST and k-t SENSE using
new-k-best-dection
- 研究MIMO系统检测算法理论及其实现方法的基础上，对已证明较优的算法进行结合和改进，提出了一种改进的K—Best检测算法及其实现方案，并通过仿真验证了方案的可行性。该算法采用预测技术和并行排序相结合的方法，降低了计算复杂度；采用并行流水线结构实现，节省了处理时-Basic research MIMO system detection algorithm theory and its implementation methods, have proven to be the optimum com
improve-k-best
- 研究MIMO系统检测算法理论及其实现方法的基础上，对已证明较优的算法进行结合和改进，提出了一种改进的K—Best检测算法及其实现方案，并通过仿真验证了方案的可行性。该算法采用预测技术和并行排序相结合的方法，降低了计算复杂度；采用并行流水线结构实现，节省了处理时间；并对方案在xilinx公司的Virtex_5系列n)GA中的资源使用情况进行了统计。研究表明，实现方案可以用于MIMO系统检测算法的硬件实现。-Basic research MIMO system detection algorithm
k-best-sphere-decoding-algorithm
- 通过对广度优先策略中有恒定复杂度的层排序k- best 球译码算法进行分析,提出一种每节点保留可变扩展节点的层排序 k-best 球形译码算法( k-best SDA II) , 在64QAM 调制及每层保留 8 节点的实数 SDA 模型下, 通过仿真的方法得出了保留恒定扩展节点的 k-best SDA 当每节点保留扩展节点数大于等于 2 时, 性能基本不变( k-bestSDA I)-Through the breadth-first strategy in a constant layer
k-best-for-rank-deficient
- 研究无线信道优化问题, 由于非确定性 M MIO系统的信道矩阵行不满秩特性, 导致 MM SE- GDFE左预处理后引入了强干扰, 使得传统 K- Best算法产生了误码平层效应。为了消除误码平层效应,提出部分最大似然和 K - Best结合算法, 并在初始检测的强干扰区域利用部分最大似然算法获得局部最优解, 降低强干扰对系统的影响消除误码平层效应, 进而在局部最优解的基础上利用 K- Best算法完成剩余符号的检测, 保证 M MIO 检测器的软输出特性。仿真结果表明算法消除了传统 K- Be
K-line-communication-description
- K-Line (ISO9141-2) official descr iptions
K-MeansDistance
- In this paper, we try to find out the efficiency of K-means algorithm with respect to different distance measures-In this paper, we try to find out the efficiency of K-means algorithm with respect to different distance measures