卡通自动生成模块采用基于样本学习的方法生成具有特定艺术风格的卡通头像。
An example-based approach is taken by Cartoon generator to generate the Cartoon face while capturing an artist style.
其次，提出了基于属性效用函数估计的学习样本构造方法，从决策问题本身抽取学习样本。
Secondly, to extract learning samples from the MADM problem, an approach to estimate the utility functions for attributes is presented.
仿真结果表明，当飞行参数间不存在确定的函数关系时，采用基于样本学习的飞行参数估计方法可行、有效。
The results of simulation show that the method is valid and efficient in the case of there is not affirmatory relationship among the flight parameters.
针对这一缺陷，将基于小样本理论的支持向量机学习方法应用到发动机的故障诊断中。
To solve the problem of lack of fault engine sample, support vector machines, which is a method based on small sample theory is applied.
以14个边坡工程的稳定状况作为学习样本和预测样本，讨论了基于神经网络技术的黄土边坡稳定性分析方法及其有效性。
Regarding stability conditions of 14 slopes as study samples and predicting samples, we discuss the stability analysis method and its usefulness based on neural network technology.
基于统计学习理论中结构风险最小化原则的支持向量机是易于小样本的机器学习方法。
Support vector machine (SVM) based on the structural risk minimization of statistical learning theory is a method of machine learning for small sample set.
支持向量机方法基于小学习样本条件下，通过寻求结构风险最小，以期获得良好的分类效果和泛化能力。
The SVM method is based on seeking on the Structural Risk Minimization by few learning samples supporting, and it has important feature such as good generalization and classification performance, etc.
建立在统计学习理论基础之上的支持向量机(SVM)，是一种基于结构风险最小的小样本机器学习方法。
Support vector machine (SVM) is a novel and powerful learning method which is derived based on statistical learning theory (SLT) and the structural risk minimization principle.
提出了一种基于统计的学习样本生成方法，使样本生成问题规范化。
An improved BP algorithm and a learning sample generation method based on statistics are performed.
神经网络的理论基础是最小化经验误差，这种基于传统的渐进理论的学习方法，在训练样本点无穷多时是适用的。
Because neural network is based upon empirical risk minimization and asymptotic theories, it is suitable to deal with situations where the amount of samples is tremendous and even infinite.
在该演化算法中，采取训练正反类样本加权模板的方法来构造各个弱学习分类器，克服了常规的基于单一特征构造弱分类器的不足。
The algorithm used weighted templates to structure each weak learning classifier, which overcame the shortcoming of structuring classifier by using a single feature.
支持向量机是一种基于统计学习理论的机器学习方法，该理论主要研究在有限样本下的学习问题。
Support vector machine is a kind of machine learning algorithm based on statistical learning theory which mainly researches the learning of limited number of samples.
支持向量机(SVM)是基于统计学习理论的一种智能学习方法，可以用来解决样本空间的高度非线性的模式识别等问题。
Support Vector Machine (SVM) is an intellectual learning method based on the statistics theory. The SVM can solve problems of complicated nonlinear pattern recognition of spatial samples.
该方法基于均匀设计获得的样本进行神经网络学习，用模式–遗传–神经网络进行岩体流变参数的最优辩识。
The samples produced by uniform design are used to train NN whose architecture is determined in global optimization by pattern-genetic algorithm(PGA).
支持向量机(SVM)方法是基于统计学理论的一种新的机器学习方法，对解决小样本条件下的非线性问题非常有效。
The Support Vector machine (SVM) is a new machine learning method based on the statistical learning theory and it is very useful to solve nonlinear problems of short time series.
支持向量机(SVM)方法是基于统计学理论的一种新的机器学习方法，对解决小样本条件下的非线性问题非常有效。
The Support Vector machine (SVM) is a new machine learning method based on the statistical learning theory and it is very useful to solve nonlinear problems of short time series.
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