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- high dimension sample 高维样本
- As for the undivided linear sample space, the kernel function is needed to map onto another high dimension linear space. 对于线性不可分的样本空间,需要寻找核函数,将线性不可分的样本集映射到另一个高维线性空间。
- Support Vector Machine(SVMs) is a novel machine learning method based on statistical learning theory(SLT).SVM is powerful for the problem with small sample,nonlinear and high dimension. 支持向量机是以统计学习理论为基础发展起来的新的通用学习方法,较好地解决了小样本、高维数、非线性等学习问题。
- This method can be employed to detect the outliers of datasets with the number of outliers being unknown.Appling it into the “small sample,high dimension” microarray datasets achieves a good result. 算法应用于“小样本,高维度”的基因微阵列数据集进行样本孤立点检测取得了很好效果,证明了此方法的有效性。
- The cable tray is made of aluminum metal alloy by extruding, high dimension precision, high intensity, artistic shape. 桥架采用铝合金型材挤压成型,尺寸精度高;强度好,外形美观;
- It can reduce classifier space with high dimension,and then learn a combiner in lower dimension. 该方法将高维分类器空间压缩至低维分类器空间,并在该空间内学习集成器。
- It is valid for high dimension dataset, and it can find outliers accurately and validly. 实验结果表明,该算法能够识别任意形状的聚类,对高维数据有效,能够很好的识别出孤立点。
- Support vector machines (SVMs) are employed in that SVMs can avoid the dimension curse problem and are best fit for the analysis of microarray data with small sample size and high dimensionality. 该方法能够有效避免传统机器学习算法应用中的维数恶化问题,最适合处理像癌症微阵列数据这样的高维小样本问题。因此,在我们的算法中,支持向量机分类器被使用。
- The artifact can be decreased obviously when using the calibrated data for big dimension samples to reconstruct CT images in the actual experiment. 在具体的实验过程中,通过对大尺寸样品投影数据的校正,明显减少了重建图像的佃伪影。
- For the hexagonal work parts with high dimension accuracy requirement in diagonal, the sharp or round edge at the corner produced with less waste punching arrangement will influence its dimension precision. 对角线尺寸有精度要求的六角工件,在采用少无废料排样冲裁加工时,在其角尖部位产生圆角或披锋(刺)影响其尺寸精度。
- Humanoid beings exist in higher dimensions. 有人特点的生命存在于更高维度内。
- The data space was mapped to high dimension feature space with Mercer kernel function, and fuzzy kernel learning vector quantization (FKLVQ) was done on the feature space to obtain the effective and stable clustering weight vectors. 该方法通过Mercer核,将数据空间映射到高维特征空间,并在此特征空间上进行FKLVQ学习获取数据空间有效且稳定的聚类权矢量,然后在特征空间和输出空间上仅针对各空间的数据样本和它们各自的聚类权矢量进行Sammon非线性核映射。
- The support vector machine is a new statistical learning method. It can solve small-sample, non-linear and high dimension problems by using structural risk minimization (SRM) instead of empirical risk minimization (ERM). 支持向量机则是一门新的统计模式分类方法,支持向量机用结构风险最小化原则代替了经验风险最小化原则,同传统的模式识别方法相比,支持向量机在小样本、非线性及高维模式识别问题中表现出许多特有的优势。
- With the aborative description of geometry and analytics,applying Cartan Method,gets the Euler equation of weakly harmonic maps from high dimension Riemann manifold to homogeneous space. 借助于几何上与分析上的精细刻画;利用卡坦方法;得到高维Riemann流形到齐次空间弱调和映射体现的具体Euler方程形式.
- The example of tooling shape design using this algorithm shows that high dimension accuracies can be achieved for general revolving parts with arbitrary cross-section shapes after two iterations. 实际计算结果表明,该算法具有较快的迭代速度,对一般形状的回转体零件可以在2次迭代后获得较高的零件精度。
- If you spend one day in a higher dimension you would come back one year later. If we go lower, time is more dense. 如果你在一个较高维度停留一天,你会需要一年之后才返回。如果我们去低维度,时间会变得更加密集。
- Maybe the amount of original features is very large, sample objects maybe situate in high dimensional space, but they can be expressed in low dimensional space by mapping or transforming method or etc. 原始特征的数量可能很大,样本可能是处于一个高维空间中,通过映射或变换等方法可以用低维空间来表示样本。
- In this paper, analyzes two dimensional characteristics of the spectrum of HRI luminance signals mainly and gradually builds up its mathematical model and three dimensional sample spectrum model. 主要分析了HRI亮度信号的空间频谱特性,并逐步建立其二维频谱的数学模型和二维抽样谱模型。
- SVM solves practical problems such as small samples, nonlinearity, over learning, high dimension and local minima, which exist in most of learning methods, and has high generalization. 它较好地解决了以往困扰很多学习方法的小样本、非线性、过学习、高维数、局部极小点等实际问题,具有很强的推广能力。
- However, the classified proteomic data isn’t gotten easily, because of high dimension and noise.So, to analyze the data preprocess of proteomics is a very important part in this thesis. 但是,质谱技术在分类工作中往往受到资料维度过高与杂讯干扰所困扰,所以蛋白质体分析的前置处理在本文中是很重要的一环。