论述了该方法的原理、鲁棒性和特征向量维数选取。
We state the principle of this method, while the robust and the eigenvector dimension are discussed.
传统的PCA变换是对图像向量的分析，但向量维数一般都很高。
在自动文本分类系统中，特征选择是有效降低文本向量维数的一种方法。
Feature selection is a valid method to reduce the dimension of text vector in automatic text categorization system.
提出重构样本库的概念及构建算法，获得稀疏样本库，减少特征向量维数。
To get sparse sample library and reduce eigenvector dimension, a new concept of reconstructing sample library and its corresponding algorithm are introduced and presented, respectively.
在诊断模型中，应用APEX网络提取分类信息，压缩向量空间维数，利用前馈网络建立其类型识别函数。
In the model, using APEX network extracts classification information and condenses vector space dimensions, making use of feedforward network establishes the classification recognition function.
为了给出图像类推算法的适用范围，提出了一种新的基于分形维数向量的图像的相似性度度量算子。
To give applicable range of image analogies algorithm, based on the fractal dimension vector, a new operator used to measure the image similarity is proposed.
支持向量机(SVM)作为一种新型的非线性建模方法，适合于处理小样本和高维数的建模问题。
Support vector machines (SVM) is a new nonlinear modeling method which is suitable for solving small samples and high dimension modeling problems.
传统的统计方法不能够有效的处理如此高维数的特征向量，但是支持向量机就能够解决这一问题。
Traditional statistical method cannot deal with feature vectors with so high dimensions efficiently, but SVM (Supported vector Machine) could resolve this problem.
在软测量建模过程中，基于支持向量机的算法能较好地解决小样本、非线性、高维数、局部极小点等问题。
In model establishment of soft-sensing, the problems of small sample, non-linearity, high dimensions and local minimal value can be well solved by support vector machine algorithm.
一个支持向量机的支持向量数相关的VC维是怎样的？有一个公式，关于这两个量？
How is the VC dimension of a support-vector machine related to its number of support vectors? Is there a formula relating these two quantities?
实验结果表明，该信号测量和抽取算法在有效保留信号精度的同时，显蓍地减少了信号特征向量的维数。
Experimental results show that the feature extraction approach remarkably reduces the dimensionality of the input vector while the characteristics of the signals have been reserved.
同时针对神经网络易于陷入局部极值、结构难以确定和泛化能力较差的缺点，引入了能很好解决小样本、非线性和高维数问题的支持向量回归机来进行油气田开发指标的预测；
The method of support vector regression which can well resolve the problem with the insufficient swatch, nonlinear and high dimension is introduction to predict the development index of gas-field.
支持向量机是一种新的机器学习方法，它具有推广能力强、非线性和高维数等一系列优点。
Support Vector machine (SVM) is a new method of machine learning. It has some advantages such as generalization ability, nonlinear and high dimensions.
机械系统的欠驱动特性是指系统控制输入向量空间的维数小于系统广义坐标向量空间维数的情况。
The underactuation of mechanical systems means that the dimension of the control vectors of systems is less than that of the configuration vector of systems.
增广向量法通过扩充输入、状态、输出等向量的维数及系统系数矩阵维数构造增广系统，使复杂问题简单化。
An augmenting system is designed to extend dimension of input, state, output and the coefficient matrixes in the method of augmenting vectors in order to predigest complicated problem.
研究了支持向量机参数(核函数、惩罚因子c)和影像特征维数对航空影像分割与分类的影响。
This paper researches the parameters (kernel, penalty parameter c) of SVM and the dimension of feature, which influence aerial image segmentation and classification.
压缩特征向量的维数，在较低维特征空间中进行分类器设计是特征选择与提取的目的。
To compress the dimensions of characteristic vector and make the design of classificatory in the characteristic space with lower dimensions is the purpose of characteristic selection and extraction.
计算的复杂度不再取决于空间维数，而是取决于样本数，尤其是样本中的支持向量数。
Thus the computational complexity is no longer depend on space dimension, but the number of samples, especially the number of support vectors in each sample.
该方法可降低数据空间维数和支持向量机处理过程的复杂度，但不会降低分类和预测性能。
The method can reduce the dimensions of the data set and the complexity of the model of SVMs, and doesn't affect its classification and prediction performance.
在采用SVM算法的文本分类中，由于文本所表征的向量空间维数通常非常巨大，因此在训练过程中将耗费大量的系统资源。
For text classification based on SVM learning algorithm, usually there is an abundance of training data, which will cost a lot of computing resources in training process.
为解决传统索引方法对高维数据索引时存在的维数灾难问题，提出一种多分辨率向量近似方法。
The Vector Approximation File approach overcomes some of the difficulties of dimensionality curse, but it cant support the quadratic form metric.
结果表明，支持向量机比BP神经网络有较高的预测精度，并且具有小样本、高维数及非线性等优点。
The result indicates, that the accuracy of predictions by SVMs is better then the predictions of BP neural networks, and with some merits of small samples, multi-dimensions and non-linear.
该FFT算法适合于维数为任意整数的情况，当维数为1时，算法退化为著名的频域抽取向量基2 FFT算法。
This FFT algorithm can be used with arbitrary integer dimensions. For 1-dimension, the algorithm will be simplified as the well-known DIF vector radix 2 FFT.
该算法和传统色彩量化算法相比，降低了特征向量的维数，计算量小，受光照强度影响较小，提高了检索的性能。
Then we train SVM classifier using visual features such as color, texture, and shape. Experimental results show that this approach can improve precision and recall effectively.
该算法和传统色彩量化算法相比，降低了特征向量的维数，计算量小，受光照强度影响较小，提高了检索的性能。
Then we train SVM classifier using visual features such as color, texture, and shape. Experimental results show that this approach can improve precision and recall effectively.
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