1) sequential number theoretic optimization
序贯寻优
1.
In recogniting the state of art status in the discipline, a new approach for global optimization of processes, based on sequential number theoretic optimization (SNTO), is proposed.
基于此 ,提出一种并行序贯寻优 (SNTO)方法 。
2) sequential optimization
序贯优化
1.
Combining the tolerance-cost model with the optimal design model this paper established an optimal model for product economic design and performed a sequential optimization.
介绍一种近似模拟产品性能指标和设计变量之间函数关系的响应曲面建模方法(RSM),进行容差设计并建立容差-成本模型,将容差-成本模型与设计最优化模型相结合,即建立产品的经济设计最优化模型并进行序贯优化。
2.
This model is then used in the sequential optimization.
将容差——成本模型与设计最优化模型相结合,即建立产品的经济设计最优化模型并进行序贯优化。
3) Sequential Projection Pursuit
序贯役影寻踪
5) sequential minimal optimization
序贯最小优化
1.
After the analysis of the difficulties in training SVM and a survey of some popular methods and their relationship, sequential minimal optimization (SMO) and its improved versions are discussed in detail.
该文从分析SVM训练问题的实质和难点出发,结合目前一些主要的SVM训练方法及它们之间的联系,重点阐述当前最有代表性的一种算法——序贯最小优化(SMO)算法及其改进算法。
2.
At present sequential minimal optimization (SMO) algorithm is a quite efficient method for training large-scale support vector machines (SVM).
序贯最小优化(sequential minimal optimization,简称SMO)算法是目前解决大量数据下支持向量机(support vector machine,简称SVM)训练问题的一种十分有效的方法,但是确定工作集的可行方向策略会降低缓存的效率。
3.
Working set selection is an important step in the sequential minimal optimization (SMO) type methods for training support vector machine (SVM).
针对标准序贯最小优化(sequential minimal optimization,SMO)算法采用可行方向工作集选择策略所带来的缓存命中率低下问题,给出了SMO类型算法每次迭代所带来的目标函数下降量的二阶表达式,并据此提出了一种改进的工作集选择策略。
6) Sequential Minimal Optimization(SMO)
序贯最小优化
1.
When Keerthi s Sequential Minimal Optimization(SMO) algorithm is applied to the classification of unbalanced datasets,it not only leads to a poor classification performance but makes the result unstable.
为了解决Keerthi改进的序贯最小优化(SMO)算法在处理非平衡数据集时,整体分类性能较低、稳定性差等问题,对两个类别施加不同的惩罚系数的方法对算法作进一步改进,同时给出计算公式及算法步骤。
2.
The algorithm searched for the optimal distribution of support vectors unlike the sequential minimal optimization(SMO) algorithm focusing on the convergence of object function.
不同于序贯最小优化(SMO)收敛目标函数的思路,该算法寻找支持向量在最优状态下的分布,对Karush-Kuhn-Tucker(KKT)条件不敏感,并可获得解析的最优值。
补充资料：不合格百分率计数检验的序贯抽样方案
不合格百分率计数检验的序贯抽样方案
sequential sampling plans for inspection by attributes for percent nonconforming
加hege比ifen.o Jishu Jianvan dex叩uanC伙泪四叩fa叩’On不合格百分率计数检验的序贯抽样方案(se-quenti吐~plingp】川15 for inspection场attributesforpe二ni nonconfo丽ng)逐个地(或成组地)抽取个体，但事先并不固定它们的个数，根据事先规定的规则，直到可以做出接收或拒收批的决定为止。作为代表性的是IEC 1 123:l卯l标准。 设在n.抽试产品中出现;个不合格，且抽样方案给出:，h两数。判决规则如下:①如;n一h〕r，则接收;②如:n一h
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条