feature space
英 [ˈfiːtʃə(r) speɪs]
美 [ˈfiːtʃər speɪs]
网络 特征空间; 高维特征空间; 转换到特征空间; 特徵空间; 度的特徵空间
双语例句
- Sample data were extended to the high dimensional feature space by kernel method and Maximum A Posteriori ( MAP) was obtained by Bayesian classifier, and then classification result could be gotten.
利用核技术将样本数据扩展到高维特征空间中，采用贝叶斯分类器得到最大后验概率，进而得到分类结果。 - First, the sample datasets were mapped into a high-dimensional feature space through a kernel function. Then, all decision functions were obtained using the proposed method.
首先将样本数据通过核函数映射到高维数据特征空间，然后利用提出的方法找出各个判决函数； - Research on Basic Methods of Construction of Feature Space Related to Indefinite Kernels
非正定核特征空间构造基本方法的研究 - Research on Blog Identification Based on Comprehensive Feature Space
基于综合特征空间的Blog网页识别方法研究 - Latent Semantic Index and Hierarchical Clustering Based Feature Space Reconstruction Method and Application Research Associative meaning lies on the bottom layer and it conveys intuitive and aesthetic meaning.
基于潜在语义索引及层次聚类特征空间重构方法与应用研究联想意义潜伏在语义的底层，是审美认知中感性思维的载体。 - Research on Classification Method and Its Application in Risk Decision-Making with Feature Space Heterogeneity
考虑特征变量异质性的分类方法及其在风险决策中的应用研究 - Firstly, we discuss the modeling method based on hybrid entropy theory, and use the statistical data of feature space and fuzzy classifier to obtain modeling parameters.
在分析混合熵模型基本原理的基础之上，提出利用特征空间的和模糊分类器的统计数据来建立信息熵、模糊熵以及混合熵的方法。 - In this paper,. we analyze these peculiarities, and conduct experiments on a corpus of1000 web page to contrasts three feature selection techniques for dimensionality reduction of the feature space.
本文分析了网页内部特征，并在1000篇网页规模语料库的基础上，通过试验对比了三种特征选择方法对高维特征向量空间进行降维的效果。 - We proposed adaptive semi supervised marginal fisher analysis ( ASMFA) by which the consistency constraint is defined in the original feature space and the expected low-dimensional feature space.
提出了自适应半监督边界费舍尔分析算法，它将一致性约束定义在原特征空间和期望低维特征空间中。 - In classification, feature space heterogeneity is an important kind of data characteristics, and impacts significantly on the classification performance.
在分类问题中，特征变量异质性是一种重要的数据特性，对于分类方法的应用结果具有显著影响。 - In this study, a nearest feature space embedding ( called NFS embedding) algorithm is proposed for face recognition.
在本研究中，我们提出了一种基于最近特徵空间转换法的人脸辨识演算法。 - Study on feature space of bands and their reconstructed vectors of remote sensing data in Tarim River Basin
塔里木河流域遥感波段及重构向量的特征空间分析 - Study of Modeling Based on Kernel Method in Sparse Feature Space
基于稀疏特征空间的核方法建模研究 - This paper presents a multiclass neural network classifier to learn disjunctive fuzzy information in the feature space.
本篇论文提出一个类神经网路分类器来学习多类的分离模糊资讯。 - Estimation of soil moisture from Ts-EVI feature space
基于Ts-EVI特征空间的土壤水分估算 - Conventional algorithms assumed that the consistency constraint is defined on the original feature space. However, the original feature space is not the best for defining consistency.
传统的算法一致性约束是定义在原特征空间中，但是在原特征空间中定义的一致性不是最好的。 - And an orthonormal basis of the kernel feature space is constructed.
同时构造该核特征空间的一组正交基。 - The mismatch can be mapped into three spaces, i.e. signal space, feature space and model space.
噪声所带来的失配可以映射到信号空间、特征空间和模型空间，因此抗噪声的方法也可以从这三个空间来考虑。 - A new feature space transformation algorithm in color image is presented.
本文提出了一种彩色图像特征空间变换算法。 - The method establishes the feature space model according to the conversion relations of color images and gray images.
该方法根据彩色图像到灰度图像的转换关系，建立特征空间模型； - Probability of eigenvalue in feature space is taken as the template information.
该算法首先将目标在特征空间中的特征值的概率作为模板信息； - An approach to track target of the aerial image using Gabor deformable template is presented in feature space.
在特征空间中利用Gabor小波和可变形模板实现了航拍图像中目标的跟踪。 - This paper employs feature selection theory and pattern aggregation theory to reduce feature space dimension.
应用特征选取和模式聚合理论以降低特征空间维数。 - High accuracy DEMs is an important data in study of soil erosion and terrain feature space analysis.
高精度数字高程模型（DEM）是水土流失规律研究与流域地形空间分析中重要的空间数据。 - Finding optimum feature extracting algorithm and classes 'centres in feature space are two important problems.
寻找最优的特征提取算法和特征空间中最优的聚类中心是目标识别领域的两个重要问题。 - In this paper, a novel fuzzy membership function model is presented based on the feature space.
文章提出了基于特征空间的模糊隶属度函数模型。 - The shape of the object contains a lot of visual information, becoming the most commonly used feature space.
目标的形状包含了大量的视觉信息，成为最常用的特征空间。 - However, this method requires do a variety of mapping and transformation in high-dimensional original feature space.
但由于这种方法需要对高维的原始特征空间进行各种映射与变换，导致时间复杂度变高，降低了算法效率。 - In Chinese text categorization, feature selection is a way which commonly used to reduce the feature space dimension.
在中文文本分类中，特征选择是降低特征空间维数的一种常用方式。 - The text feature space is the very complex spatial structure. It is not equality between each coordinate.
文本特征空间是个非常复杂的空间结构，而各个坐标之间也并不是平等的，概念之间的语义有不同程度联系。