1)  multiple regressions
多类回归
1.
Through the study on mathematical geology in Kekeya area, we analyze the trace elements by means of discriminant analysis using multiple regressions.
本文采用多类回归判别方法对柯克亚地区地层微量元素进行回归判别分析 ,建立回归判别模式 ,以求找到一种较好解决“哑地层”划分对比的新方
2)  multi-class
多类
1.
Supported by this feature space,a new multi-class blind steganalyzer for JPEG images is presented.
为准确判断一幅JPEG图像使用了何种隐密软件,针对JPEG隐密软件可能采用的DCT域隐密操作,建立了基于微观模板的统计特征空间,并在此基础上提出了一种多类JPEG图像盲隐密分析方法。
2.
How to extend it for multi-class classification effectively is still an on-going research issue.
传统的支撑向量机是两类的分类器,如何将其有效地推广到多类问题仍是一个有待研究的问题。
3.
How to effectively extend it for multi-class classification is still an on-going research issue.
传统的支持向量机是基于两类问题提出的,如何将其有效的推广至多类问题仍是一个有待研究的问题。
3)  Different crowds
多类人群
4)  multiclass classification
多类分类
1.
New perfect performance multiclass classification algorithm based on KFDA;
基于核Fisher判决分析的高性能多类分类算法
2.
In this paper, based on the analysis of the core concepts in the Kernel method, the basic principles of the multiclass classification based on the one-class classification were studied, and the reliability function applied to the multiclass classification was put forward, which makes the classif.
在分析一种非线性数据处理新方法的核心概念基础上 ,研究了基于一类分类方法的多类分类基本原理 ,提出了应用于多类分类的可信度函数 ,使聚类与分类的结果更具有可信度 。
3.
A novel multiclass classification method based on high dimensional map and the weight-learning method of Rosenblatt network is put forward in this paper,which can solve linearly inseparable problem.
利用Rosenblatt感知器网络的权值学习方法,提出一种解决线性不可分样本的多类分类方法。
5)  multi-class
多类分类
1.
The multi-class SVM has recently been extensively applied in the field of pattern recognition.
目前SVM多类分类方法在模式识别领域得到了广泛使用。
6)  multiple user classes
多类用户
1.
A travel time reliability based traffic assignment model with multiple user classes was formulated through a variational inequality(VI) approach.
假设出行者基于期望行程时间和行程时间可靠性的均衡选择路径,根据出行者对待行程时间可靠性的不同态度,将其路径选择行为分类,建立了基于行程时间可靠性的多类用户交通分配的变分不等式模型。
参考词条
补充资料:多波长线性回归
分子式:
CAS号:

性质:对多组分样品在多个波长进行吸光度测量,基于吸光度加和性原理,建立线性回归方程,求解各组分的含量。这种方法在多组分光度分析中广泛应用。

说明:补充资料仅用于学习参考,请勿用于其它任何用途。