1) Non-Supervisory Pattern Classification
无监督模式分类
2) unsupervised classification
无监督分类
1.
To avoid the disadvantage of getting into local optimum solution with general numerical computation methods in the general independent component analysis and the restriction of neuron activation functions of neural learning algorithm,an improved model of independent component analysis(ICA) based on genetic algorithm was proposed for the unsupervised classification of hyperspectral data.
针对独立成分分析在使用常规数值求解时容易陷入局部最优解的问题,以及采用神经学习算法时神经元激活函数的限制问题,将遗传算法与独立成分分析相结合,并对模型进行改进,提出了适合于高光谱数据无监督分类的模型。
2.
In order to classify the data of Hyperspectral remote sensing images automatically without prior knowledge,an unsupervised classification algorithm is presented based on the conception of convex geometry and spectral features in this paper.
为了实现对无任何先验知识的高光谱遥感数据的全自动分类,提出了一种关于高光谱图像的无监督分类算法。
3.
In this study,a novel artificial immune system algorithm for unsupervised classification and recognition is proposed by using a novel manifold distance based dissimilarity measure which can measure the geodesic distance along the manifold.
将一种新的流形距离作为相似性度量测度,提出了一种用于无监督分类与识别的人工免疫系统方法。
3) unsupervised pattern
无监督模式
1.
Extending the optimal set of discriminant vectors for an unsupervised pattern;
最佳鉴别矢量集在无监督模式下的扩展
4) unsupervised character classification
字符无监督分类
5) unsupervised classification of image
图像无监督分类
6) unsupervised classification
无监督分类法
补充资料：有监督模式识别
分子式：
CAS号：
性质：在模式识别中，用一组已知类别的样本作为训练集，建立数学模型，再用已建立的模型对未知样本进行判别，以确定未知样本应归属的类别。
CAS号：
性质：在模式识别中，用一组已知类别的样本作为训练集，建立数学模型，再用已建立的模型对未知样本进行判别，以确定未知样本应归属的类别。
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