算法分类 meaning in Chinese
algorithm classification
Examples
- We evaluate error rate , scalability , time , tree nodes numbers by 12 - cross validation method . experiment has demonstrated that new algorithm greatly reduces the error rate and has good scalability at the same time
实验中采用12交又验证方法，对算法分类准确率、伸缩性、时间、树节点个数等几个指标进行评估。 - A learning algorithm of compressed candidates based on bayesia belief network is developed to solve slow running problem of traditional bayesian belief network constructing algorithm
摘要针对传统算法分类速度较慢的不足，改进传统算法中候选变量的搜索方式，提出用依赖度量函数测量变量之间的依赖程度，得出压缩候选的贝叶斯信念网络构造算法。 - Then we describe the lastest researches and developments on multicast congestion control algorithms and classify them from different aspects . furthermore , different algorithms are analyzed and compared , and some problems are pointed out
然后讨论了组播拥塞控制算法分类的标准，比较和分析了现有组播拥塞控制算法的优缺点，指出了其中的不足之处。 - There are two different visualization approaches of 3d - data sets , one is surface rendering algorithm , the other is volume rendering algorithm . the latter is the emphasis of the paper . the paper describes its optical modek algorithm classification and discusses its future applications and problems to be solved
体绘制算法是本文的研究重点，本文介绍了体绘制算法的光照模型、算法分类和发展方向，并以光线投射算法为例，详细的论述了体绘制算法的原理、流程、关键技术。 - In algorithms , classification algorithms are divided into two cases : one for known statistical distribution model and the other for unknown statistical distribution model . four classification algorithms , the bata - prime statistic model fusing quadratic gamma classifier , based on sar image rcs reconstruction and space position mode , on the mixed double hint layers rbfn ( mdhrbfn ) model and on the self - adapt fuzzy rbfn ( afrbfn ) model , are derived . the problems , including how to further improving the class ratio of the bayes decision , decreasing the dependence on the statistical model and directly providing the adapted algorithm with samples , are solved
提出了基于径向基函数神经网络( rbfn )的双隐层混合网络( mdhrbfn )模型，解决了标准神经网络在具体sar图像地物分类中分类类别数目不够和分类精度差的问题；提出了基于模糊推理系统的自适应模糊rbfn分类( afrbfn )模型，兼顾通用性与精确性，增强人机交互能力，进一步提高了算法分类率。