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mixed classification
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  “mixed classification”译为未确定词的双语例句
     Analysis of Variance for a Mixed Classification Model
     一类混合分类的方差分析模型
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     (4)Propose one kind of mixed classification model of Web text based on rough set and KNN.
     (4) 提出一种基于粗糙集和KNN的混合文本分类模型。
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     For a mixed classification model of the form as y_(ijkl)=μ+α_i+β_j+y(k(ij))+e_(ijkl) i=l,…a;
     作者对一类混合分类的方差分析模型 y_ijkl=μ+α_i+β_j+γ_(k(ij))+e_(ijkl) i=1,…,a;
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     APPLICATION OF FUZZY MIXED CLASSIFICATION IN SEQUENCE OF THE POTENTIAL POISON OF HEAVY METALS
     用模糊混合聚类法对重金属潜在毒性排序
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     Through attempting to introduce into decision tree idea, firstly picked-up forest styles which are easily mixed classification each other, then separating the mixed styles and using mahalanobis-Distance to classify the residual image, lastly merging the each forest styles.
     通过尝试引进决策树思想,先提取出容易混分的地物类型,再对其混分类型进行分离,并用马氏距离法对剩余影像进行计算机分类,最后归并各个地物类型。
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  mixed classification
The results are used to establish four matrix population models, based respectively on the classification of the individuals by age, weight and stage and on a mixed classification.
      
There is a third group which includes epilepsy syndromes of uncertain or mixed classification such as neonatal seizures, febrile seizures etc.
      


The artical deals with the Fuzzy Mixed Classification i,e,the combination of the principles of the maximum matrix element and the FuzzyISODATA Classification. This method is applied to analize the physicaland chemical nature of 51 elements of heavy metals through computer.The selection of data related to the poison is employed to calculate, classify and sequence the potential poison of the heavy metals,

本文采用模糊混合聚类方法,即最大矩阵元原理与模糊ISODATA聚类分析相结合的方法,在计算机上对51个重金属元素的物理、化学性质数据进行了分析,并用筛选出的十项与毒性有关的数据对重金属潜在毒性进行了计算、分类和排序。

For a mixed classification model of the form as y_(ijkl)=μ+α_i+β_j+y(k(ij))+e_(ijkl) i=l,…a; j=l,…, b; k=l,…,c; l=l,…,d in this paper, F-statistics, tcsting hypotheses such as H_l:y_(1(ij))=y_(2(ij))=…=y_(c(ij)), i=1,…,a; j=1, …, b; H_2:α_1+■_(·(1·))=α_2+■_(·(2·))=…=α_a+■_(·(a·)) etc., are ob tained, using general theory of linear model. To illustrate the application of this approach, a numerical example is also given.

作者对一类混合分类的方差分析模型 y_ijkl=μ+α_i+β_j+γ_(k(ij))+e_(ijkl) i=1,…,a;j=1,…,b;k=1,…,c;l=1,…,d其中c_(ijkl)~N(O,σ~2),且相互独立,应用线性模型的一般理论,给出各假设检验的统计量,并通过对一个例子的计算加以说明。

Multicategory recoginition is very important to practicality of remotely-sensed image classification。This paper has presented a mixed classification methodintegrating multi-layer neuron network and unsupervised classification algorithm,Atthe first step,a multi-layer neuron network is used and the result serves as inputfor the unsupervised classification at the second step.The number of patterns thatcan be recognized is increased from 10 to 30.Applying this algorithm to SPOTremotely-sensed image...

Multicategory recoginition is very important to practicality of remotely-sensed image classification。This paper has presented a mixed classification methodintegrating multi-layer neuron network and unsupervised classification algorithm,Atthe first step,a multi-layer neuron network is used and the result serves as inputfor the unsupervised classification at the second step.The number of patterns thatcan be recognized is increased from 10 to 30.Applying this algorithm to SPOTremotely-sensed image recognition shows it can adapt the requiry of multicategoryrecognition.

多类别识别对于遥感图像分类的实用化具有重大意义。本文提出一种由多层神经网络与无监督分类相结合的复合分类方法。第一步用多层网络对几个大类进行有监督分类,第二步将网络输出作为无监督分类的输入,对遥感图像进行细分,使得可识别的类别数从原来的10类提高到30类。对SPOT遥感图像识别的结果表明,该算法能适应多类别识别任务的要求。

 
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