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high-dimension
相关语句
  高维
    On the basis of statistical learning theory, we studied high-dimension nonlinear pattern recognition with small specimen and design of nonlinear classification of support vector network with supervised learning.
    在统计学习理论的基础上 ,研究了小样本、非线性高维模式识别 ; 研究了有监督学习支持向量网络的非线性分类设计 ;
短句来源
    On that basis,mapping of high-dimension attributes onto low-dimension attributes in attributes space is carried out by K-L transform and eliminated the correlation among the attributes so that can effectively solve the optimized issue of attributes combination.
    在此基础上 ,通过 K- L 变换将属性空间的高维属性映射为低维属性 ,且去除了属性之间的相关性 ,从而有效地解决了属性组合的优化问题。
短句来源
  高维数
    Support vector machine(SVM) is a new machine learning method based on statistical learning theory. It can process the high nonlinear problems with classification and regression. SVM not only can solve some problems,such as small-sample,over-fitting,high-dimension and local minimum,but also has higher generalization(forecasting) ability than that of the artificial neural networks.
    支持向量机(SVM)基于统计学习理论,可以处理高度非线性分类和回归等问题,不但较好地解决了小样本、过学习、高维数、局部最小等实际难题,而且具有很强的泛化(预测)能力。
短句来源
    Artificial neural network can implement a non-linear mapping for high-dimension complex data and it has been widely used in the field of pattern recognition.
    人工神经网络方法可对复杂的高维数据进行非线性映射,在模式识别领域得到越来越广泛的应用。
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  high-dimension
The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space.
      
The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space.
      
Low-and high-dimension limits of a phase separation model
      
The Belinsky-Zakharov inverse scatteringmethod is extended to a double high-dimension form.
      
Algorithms of determining maximum (in modulus) complex-conjugate eigenvalues are considered as applied to finding eigenvalues of high-dimension matrices according to the Khilenko method.
      
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Today water resource is very important in the world. The variation of annual runoff not only influences economy and people′s living standards, but also restricts the economic development. To solve these problems, accurately predicting the variation of annual runoff is indispensable to scientifically utilize water resources. Being the output of a rainfall runoff system of drainage basin, the annual runoff time series is a very complex dynamic phenomenon varying from region to region and changing with time, which...

Today water resource is very important in the world. The variation of annual runoff not only influences economy and people′s living standards, but also restricts the economic development. To solve these problems, accurately predicting the variation of annual runoff is indispensable to scientifically utilize water resources. Being the output of a rainfall runoff system of drainage basin, the annual runoff time series is a very complex dynamic phenomenon varying from region to region and changing with time, which includes lots of past information of all variations and hides many laws. The evolution treads of annual runoff time series are often time irreversible, nonlinear with weak dependence. Now traditional methods for predicting annual runoff usually use linear technique, but the forecasting precision is not satisfactory, owing to complexity of its intrinsic evolutions, and its close and complicated relationships to climate change and other hydrologic effect factors. In order to predict the high dimension complex dynamic systems of the annual runoff system, a new model-projection pursuit threshold regressive (PPTR) model is presented in this paper. A scheme of PPTR modeling is also given to reduce the computational amount, a new function of projection indexes is constructed, the relation of projection value and predicted object can be described with threshold regressive (TR) model, and it is suggested that both the function of projection indexes and the parameters of TR model can be optimized by using a real coded genetic algorithm developed by the authors. The examples of predicting annual runoff show that PPTR model is both practical and effective. PPTR model is simple and general, which overcomes the shortcomings of large amount of computation and difficulty of computer programming in traditional projection pursuit methods, benefits the more applications of projection pursuit, and gives a new approach to resolving the high dimension, nonlinear complex predictive problems.

为预测年径流这类高维复杂动力系统 ,提出了投影寻踪门限回归 (PPTR)模型。构造了新的投影指标函数 ,用门限回归 (TR)模型描述投影值与预测对象间的非线性关系 ,并用实码加速遗传算法优化投影指标函数和TR模型参数。实例的计算结果表明 ,用PPTR模型预测年径流是可行而有效的。PPTR模型简便、适用性强 ,克服了目前投影寻踪方法计算量大、编程实现困难的缺点 ,有利于投影寻踪方法的推广应用 ,为解决高维非线性复杂预测问题提供了新途径

Pattern recognition of support vector network with supervising learning is studied in this paper. On the basis of statistical learning theory, we studied high-dimension nonlinear pattern recognition with small specimen and design of nonlinear classification of support vector network with supervised learning. A hybrid algorithm for solving nonlinear optimization problem is constructed. To deal with the classification of oil and gas, we studied deeply into the extraction of feature parameters and selection...

Pattern recognition of support vector network with supervising learning is studied in this paper. On the basis of statistical learning theory, we studied high-dimension nonlinear pattern recognition with small specimen and design of nonlinear classification of support vector network with supervised learning. A hybrid algorithm for solving nonlinear optimization problem is constructed. To deal with the classification of oil and gas, we studied deeply into the extraction of feature parameters and selection of inner product function. The proposed method can satisfactorily overcome the drawbacks of under or over learning problems of neural network. Desired results have been reached by applying the method to MT data.

研究了有监督学习支持向量网络的的模式识别 ;在统计学习理论的基础上 ,研究了小样本、非线性高维模式识别 ;研究了有监督学习支持向量网络的非线性分类设计 ;设计了非线性优化问题的混合解法。针对油气识别预测的具体问题 ,对特征参数的提取和内积函数的选择进行了深入的研究。该方法能够较好地克服神经网络欠学习或过学习的弊端 ,应用于实际的MT资料 ,取得了较好的效果

Projection pursuit (PP) is a new statistic method, which is good at analyzing no n-normal and non-linear high-dimensional data. It searches for the project di rection reflecting on the structure characteristics of high-dimensional dada ob jectively by projecting and reducing dimensions, and solves "dimension curse" and non-normality and non-linearity among high-dimensions data. The article c ombines the PP technique with auto-regression model of time sequence analysis, and builds up the prediction model of...

Projection pursuit (PP) is a new statistic method, which is good at analyzing no n-normal and non-linear high-dimensional data. It searches for the project di rection reflecting on the structure characteristics of high-dimensional dada ob jectively by projecting and reducing dimensions, and solves "dimension curse" and non-normality and non-linearity among high-dimensions data. The article c ombines the PP technique with auto-regression model of time sequence analysis, and builds up the prediction model of projection pursuit auto-regression (PPAR) . PPAR model tries to realize two-dimensional forecast of magnitude and time, i .e. forecasting the magnitude and time of an event in the fixed research region, and creates the projection pursuit auto-regression model of two-dimensional s eismic time sequence. In the study, we choose first the northern Tianshan area a s the test site, and the results of the regression fitting and pretest test are good, so we could realize tow-dimensional forecast. Considering the value of fo recast practice, i.e. moderately strong earthquake, we take the whole Tianshan m ountain area as our research area. Let the magnitude thresholds of time sequence are 5.0 and 5.5 respectively, and build up the models with data of undeleted-a ftershocks and deleted-aftershocks. Comparing the two models, the latter is bet ter than the former, particularly is to forecast time sequence. Their qualified ration of pretest tests are both high, so they are available for forecasting mag nitude and time of an event.

PP投影寻踪是一种长于分析非正态、非线性的高维数据的新统计方法,它通过投影降维,客观地寻找反映高维数据结构特征的投影方向,从而解决"维数祸根"和高维数据间的非正态、非线性问题。将PP理论和时间序列分析中的自回归(AR(K))模型结合起来,建立投影寻踪自回归预测模型(PPAR),尝试实现地震震级和时间的视二维预测,即在固定研究区里,实现震级和时间二要素的预测,进而建立视二维地震时间序列的投影寻踪自回归模型。研究中首先选取北天山地区作为实验区,模型的回归拟合和外符检验效果较理想,可实现视二维预测目标。考虑到实际预测意义,即中强地震的预测,又以天山地区为研究区。令其震级序列的震级阈值分别为5.0和5.5,分别以未删除余震和删除余震的序列建立模型。对比分析表明,后者所建立的模型要优于前者的模型,特别是对时间间隔序列的预测。两者外符检验的合格率均较高,故认为对于震级和时间二要素的预测是有一定实效的。

 
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