查询词典 universal classification system

Furthermore, out of 497 fAFLP markers, 80 special bands were found to be able to distinguish the four groups from each other and may be applied for germplasm characterization and molecular assistant classification of Meretrix clam.4 Molecular classification of two species of Meretrix clam based on fAFLP and ITS sequences4.1 The results of fAFLP maker analysis of S, G and W showed that each group had their own specific loci among which there were 53 special loci in W group, much more than those of S group (14) and G group (21). Among the 53 loci, nine were all dominant loci. These unique loci could be taken as molecular markers to distinguish W from other groups. The genetic similarity indexes and distance matrix between S and G groups were 0.9585 and 0.0424 respectively, but the genetic similarity indexes and distance matrix between W group and S or G group was 0.7939 or 0.7941, and 0.2308 or 0.2305 respectively. The results revealed that significant difference existed between W and S or G groups in molecular genetic structure. The phylogenetic trees by the methods of UPGMA and NJ also indicated that S and G populations were very closely related, while W population was a relatively independent cluster, lying beyond the species which S and G belong to.4.2 The internal transcribed spacer region of the rDNA from S group, G group and W group were PCR amplified and sequenced. The results showed that the size of ITS ranged between 1266-1269bp in W group, while those in G and S groups were 1614bp and 1520bp respectively. The GC content ranged 62.32-62.62% in W group while it was 61.77% in G group. The genetic distances between three populations of W group were 0.001～0.003, but it was 0.110 or 0.147 respectively between W group and G group or S group. Phylogenetic trees by NJ method also showed that G group was very closely related to S group, while W group was a relatively independent cluster.

在457个总扩增位点中找出了53个W的特有位点，远多于S群体(14)和G(21)群体，而且在53个特有位点中有9个出现频率为100%的位点，这些位点可以作为区分其它2个群体的特征性标记；S– G群体特有的位点有112个，其中有4个位点出现频率为100%，可作为S– G群体区别于W群体的特征性标记。S群体和G群体间的遗传相似性系数为0.9585，遗传距离只有0.0424，在NJ和UPGMA法构建的亲缘关系的树状图上均首先聚在一起，说明二者的亲缘关系很近，应属于种内群体间的关系；而W与S和G的遗传相似性系数均较小(0.7939和0.7941)，相对遗传距离很大而且十分相近(0.2308和0.2305)，在亲缘关系树状图上单独分出一支，也表明W与S和G群体间的亲缘关系较远。4.2 ITS序列比较分析通过对白壳文蛤、山东文蛤和广西文蛤的ITS序列扩增电泳、PCR-RFLP分析和ITS序列分析发现，W的ITS序列长度在1266-1269 bp，而S和与G的ITS序列总长度分别为1520 bp和1614 bp；从ITS1和ITS2长度来看，W分别为739-741 bp和316-317 bp，S为895 bp和414 bp，G为987 bp和416 bp；而从ITS碱基组成来看，W的GC含量在62.32-62.62%之间，而G群体为61.77%。W的3个壳色不同群体间的遗传距离仅0.001、0.002和0.003，S与G群体间的遗传距离是0.010，说明W群体内变异很小，而S与G群体间已出现明显的遗传分化，但还均属于种内群体间的遗传变异；而W与G和S的遗传距离分别达到0.110、0.147，两个类群差异显著，已远超出种内群体间的遗传变异。

This classfier extracts the feature vector of each samples and documents to be classified by using the feature extraction algorithm. Web page was classfied by using the K-Nearest-Neighbor classification algorithm. The experimental results show that this auto classifier based on Web minning can fetch insecurity Web pages, and its classification is effective.

该自动分类器利用特征提取算法实现对每个样本的特征向量提取和待分类文本的特征向量提取，利用基于k个&最近邻&分类算法实现对网页的分类，能够提取出带有不安全信息的网页，分类效果良好。

The experiment shows the method for audio classification based on 1-state HMM could decrease the misrecognition effectively and increase the accuracy of audio classification.

实验结果表明基于单状态的HMM模型音频分类方法能有效地减少误识率，提高音频分类的精确度。

The method is shown about how the model is constructed and applied to classification. Two sorts of kernel function are applied to SVM, they are multinominal function and radial basis function. The model and algorithm are applied to some documents of China periodical document database and classification results illustrate its practicability.

文中给出了该模型的构造及应用的方法，用两种核函数作为内积回旋方案，以中国期刊网全文数据库部分文档数据为例验证了该方法的有效性。

Packet classification is a technique to match each incoming packet at a muter against a database of classifier and specify forwarding rules for the packets. The classifiers are a powerful and uniform way to implement new network services. The study on IP packet classification algorithm has practical significance and theory value.

分类器为实现因特网新业务提供了统一的方式，包分类是因特网提供一切有差别服务和其他新业务的基础，高速包分类问题是具有重要现实意义和理论价值的研究课题。

Mathematical structure of biological classification and its convincing interpretation have been an academic issue that has aroused extensive attention in the biological circle, but few have applied statistical distribution models to the study or analysis of structure of nonbiological classification, such as Soil Taxonomy.

生物系统分类的数学结构及其令人信服的解释一直以来是生物学界广泛关注的经典学术问题，然而很少有人用统计分布模型对非生物分类系统的结构进行分析与研究。

Both the relation between classification of Q and overbreak as well as underbreak and the relation classification of RMR and overbreak as well as underbreak are researched in this paper.

4正确评价岩体的质量，进行围岩分类，对研究岩体质量与洞室超欠挖之间的关系及分析和预测洞室超欠挖具有重要的意义。

In this paper, SVM is used in classification of polarimetric SAR images based on feature extraction, and effect of several important parameters of SVM on classification performance is analyzed.

该文在极化SAR特征提取的基础上，将SVM应用于极化SAR图像分类，分析了分类器参数对分类性能的影响。

Taking household\'s land use preference as classification rule, household agent types were divided though classification tree. Different choosing process of current production strategies and theirs probabilities after expiration of GFGP chosen by various types of household agents\' were put forward by experience-weighted attraction model based on bounded -rational approach. Finally, forecasting the results of households\' recultivation behaviour after the expiration of GFGP by scenario analysis method. The results show that relative policies are useful to rectify deviation of households\' bounded-rational behaviours and reinforce outcomes of GFGP.

以农户家庭户主的土地利用选择偏好作为分类依据，采用分类树方法对农户智能体类型进行划分；基于有限理性决策理论，引用经历—权重吸引模型，对农户智能体的目前生产策略决策过程和退耕还林政策期满后农户对不同生产策略的选择概率进行了分析；最后，在ERDAS环境下对退耕还林政策期满后农户的复垦行为进行了情景分析，认为政府制定并实施的相关政策措施对于纠正农户的有限理性行为偏差，维持和巩固退耕还林政策成果具有重要作用。

Attributes reduct, mining classification rules, and discretizing values of quantitative attributes are three fields in mining classification rules.

属性约简、分类规则提取、数量属性离散化是分类规则挖掘的三个方面。

The second is to cut a mere 100 calories from your diet , which is the equivalent of a tablespoon of butter.

第二个就是从你的饮食中减少一百卡路里，只是相当于一大汤匙的黄油。

This is where the Rorschach test comes in.

现在，罗氏测试就出现了。