查询词典 feature space

In this approach, the integral operator kernel functions is used to realize the nonlinear map from the raw feature space of gear vibration signals to high dimensional feature space.

该方法通过计算齿轮振动信号原始特征空间的内积核函数来实现原始特征空间到高维特征空间的非线性映射。

The integral operator kernel functions is used to realize the nonlinear map from the raw feature space of gear vibration signals to high dimensional feature space.

该方法通过计算齿轮振动信号原始特征空间的内积核函数来实现原始特征空间到高维特征空间的非线性映射。

Linear feature extraction in feature space corresponds to nonlinear feature extraction in input space.

特征空间中的线性特征提取对应于输入空间的非线性特征提取。

Prior to searching a multidimensional feature space populated with data objects, each dimension in the feature space is divided into a number of intervals.

在搜索数据对象组装的多维特征空间之前，该特征空间的每个维度被划分为多个间隔。

In this chapter, three kinds of target recognition methods are performed, which are:①Target recognition method based on the description of polarization parameter plane. The echo polarization states of target are projected onto the polarization state plane described by the ellipticity ε and the tilt angle τ of the polarization ellipse, the change of parameter following ferquency becomes the chart. According to the changing trait of the chart, the multidimensional polarization feature space of target has been contructed. Furthermore, a series of polarization feature parameters used in designing the structure of target recognition device are extracted, and they are insensible to the posture of target.②Target recognition method based on the description of Poincare polarization sphere. The echo polarization states of target expressed by Stokes vector are projected onto the Poincare polarization sphere. The conception of polarization ferquency stability, which is used in describing the dynamic distribution characteristics of the target echo polarization states on Poincare polarization sphere, has been defined. A group of polarization feature parameters used in designing the structure of target recognition device are extracted, and they are insensible to the posture of target.③Target recognition method based on the description of frequency sensitivity. In accordance with the conception of the polarization state distance defined on Poincare polarization sphere, the frequency sensibility of the physical structure property of target has been investigated, the frequency distribution feature curves in PSD domain are obtained, and targets'features are extracted by means of the curve-fitting method with Least Square Criterion.

这章具体研究了基于三种极化散射特性描述的相应的目标识别方法：①基于极化参数平面描述的目标识别方法，将目标回波极化状态投影到以极化椭圆参数，即椭圆率角ε和倾角τ表征的极化状态平面上，参数随观测频率的变化就形成了图，根据图的变化特点构造了目标的多维极化特征空间，并提取了不敏感目标姿态变化的极化特征参数组来设计目标的识别器结构；②基于Poincare极化球面描述的目标识别方法，采用Stokes矢量表征目标回波的极化状态，并将其投影到描述极化状态的Poincare极化球面上，定义了极化频率稳定度的概念用以刻画目标回波极化状态在Poincare极化球面上的动态分布信息，提取了准方位不变性的目标极化特征，最后设计了目标的识别器结构；③基于频率敏感性描述的目标识别方法，通过在Poincare极化球面上所定义的极化状态距离的概念，研究的是复杂目标物理结构特性对探测信号频率的敏感程度问题，获得了在极化状态距离下的频率分布特性曲线，采用最小二乘估计曲线拟合方法，它既用于极化特征的降维，同时又直接将拟合参数作为目标的分类特征。

The process of feature extraction is to transform the eradiate noise signal to different feature space and extract the feature vectors that reflect the category of the input sample. The extracted features are the input modes to the classifier. Through the inspecting, the author selected three methods of eradiate noise's feature extraction.

本文作者经过考察，采用了三种前期研究中较为有效的水下目标特征提取方法——基于功率谱估计的线谱特征提取方法、基于小波分析的不同频段内能量特征提取方法和基于水声信号分维特性的特征提取方法。

By using the kernel function, the input space is mapped to a high dimension feature space where the data are expected to be more separable, the initial centroids in the feature space are selected by applying the KRA algorithm, and the large and small clusters are partitioned and the outliers can be split from the large clusters iteratively after the kk-means clustering. As a result, the audit data can be clustered better. Secondly, the closed sequential patterns mining algorithm CloSpan is improved according to the restrictions that is composed of the axis properties and reference properties.

该方法通过核函数把数据样本空间映射到一个高维的特征空间，使数据在新的空间中具有更好的可分离性；在特征空间采用KRA算法选取初始聚类中心，然后在核k-means聚类的基础上，划分出大簇小簇并在大簇中分离出异类再次进行核聚类，从而不断地优化聚类结果。

The SVM method is based on the theory of structural risk minoration. It can map the sample space to a higher feature space by selecting an optimal Kernel function, and get a linear regression in this higher feature space.

中文摘要：支持向量机方法是基于结构风险最小化原理提出的，通过采用合适的核函数将样本空间映射到一个高维特征空间，再在高维特征空间进行线性回归。

The method firstly organizes support vectors in clusters in feature space, and then,it finds the pre-images of the cluster centroids in feature space to construct a reduced vector set.

此方法首先在特征空间中对支持向量进行聚类，然后寻找特征空间中的聚类中心在输入空间中的原像以形成约简向量集。

This method first maps training data into a high dimensional feature space through kernel function concept in Support Vector Machine and constructs orthogonal space in the feature space. Then it maps the data from feature space into the orthogonal space and computes Fisher criterion based global orthogonal discriminants.

首先应用支持向量机中核函数的概念，将样本隐式地映射到特征空间，然后构造特征空间的正交空间，再将特征空间样本映射到特征空间的正交空间，求解基于Fisher的全局正交鉴别矢量集。

In the negative and interrogative forms, of course, this is identical to the non-emphatic forms.

。但是，在否定句或疑问句里，这种带有"do"的方法表达的效果却没有什么强调的意思。

Go down on one's knees;kneel down

屈膝跪下。。。下跪祈祷