Fuzzy clustering is an important method in unsupervised classification.
模糊聚类是非监督分类中的一类重要方法。
A unsupervised classification method was proposed based on universal gravitation.
提出了一种基于万有引力的非监督自组织聚类算法。
The problem of unsupervised classification of remotely sensed image is considered in this paper.
该文研究了无监督遥感图像分类问题。
Made sure of the parameters and thresholds of different objects with supervised and unsupervised classification.
确定研究区内不同地物采用监督分类和无监督分类时相应的参数和阈值。
The hybrid classification can improve the efficiency of process on supervised classification or unsupervised classification.
又可以提高纯粹的监督分类或非监督分类过程的精度。
Comprehensive analysis and comparisons are given for several typical algorithms in supervised and unsupervised classification.
针对几种典型的有监督及无监督分类算法，进行了深入的分析和比较。
Clustering is an unsupervised classification method. It USES either the data similarity or PDF of data set for classification.
聚类是在非监督条件下对数据进行分类，其分类依据主要有数据之间的相似性和数据集合的分布密度函数。
Moreover, compared with the conventional unsupervised classification algorithm of hyperspectral data, the proposed algorithm is more applicable and can obtain the better precision and accuracy.
与传统高光谱无监督分类算法比较，表明该算法的适用性，并具有更高的分类精度和准确性。
Compared with the unsupervised classification and the supervised classification, the precision of multiband combination information extraction is improved to 85.5% from 25.3%, 71.6%, respectively.
与非监督分类及监督分类相比，监督分类和非监督分类结合分层提取的总体分类精度分别从25.3%、71.6%提高到85.5%。
While for terrace extraction, supervised classification has better result than unsupervised classification. The difference of total accuracy between two methods based on IKONOS image is more than 12%.
而对于IKONOS影像梯田提取，两种分类结果精度相差12%以上，监督分类结果明显优于非监督分类结果。
This would be an example of unsupervised learning in a classification context.
这将在后面成为无监督学习上下文分类的一个例子。
Supervised and unsupervised approaches for classification of remote sensing data by computer are discussed. Some primary classification results are provided.
讨论了遥感数据监督指非监督和计算机自动分类的问题，并给出了一些初步的分类结果实例。
In this paper, a new method which combines unsupervised and supervised learning strategy is put forward to construct the multi classification decision tree.
提出了一种融合无监督和监督两种学习策略生成多分类决策树的方法。
We propose a method, use Unsupervised text Clustering algorithms (UTC) to guid text classification, so as to deal with text classification without training set.
提出了一种用无监督聚类算法指导文本分类的方法，以解决没有训练集的文本分类问题。
This paper conveys the application of genetic algorithms (GA) which are used to improve unsupervised training and thereby increase the classification accuracy of remotely sensed data.
本文将遗传算法(GA)应用于非监督训练，提高了遥感数据的分类精度。
Locality Preserving Projections algorithm (LPP) is a new dimensionality reduction technique. But it is an unsupervised learning algorithm. It could not process classification effectively.
局部保持投影(LPP)是一种新的数据降维技术，但其本身是一种非监督学习算法，对于分类问题效果不是太好。
Locality Preserving Projections algorithm (LPP) is a new dimensionality reduction technique. But it is an unsupervised learning algorithm. It could not process classification effectively.
局部保持投影(LPP)是一种新的数据降维技术，但其本身是一种非监督学习算法，对于分类问题效果不是太好。
应用推荐