| 1. | It can solve small - sample learning problems better by using experiential risk minimization in place of structural risk minimization
由于采用了使用结构风险最小化原则替代经验风险最小化原则，使它较好的解决了小样本学习的问题。 |
| 2. | Structure risk minimization based weighted partial least - squared method weighted partial least - squared wpls method was proposed to achieve structure risk minimization in the partial least - squares modeling process
为了在偏最小二乘法pls建模过程中实现结构风险最小化srm ，提出基于结构风险最小化的加权偏最小二乘法wpls 。 |
| 3. | Aimed at the character of the agriculture system , the least squares support vector machine prediction model is given based on the principle of the statistical learning theory and structural risk minimization
针对农业生产系统的特征，在统计学习理论和结构风险最小化原理的基础上，建立了基于最小二乘支持向量机的时间预测模型。 |
| 4. | This method can be used to small scale recognition , like artificial neural networks , but it has stronger generalization ability because the support vector machine theory is based on the minimization principle to structure risk
该方法与人工神经网络一样适用予小规模分类，但由于支持向量机依据结构风险最小化原则，因此泛化能力更强。 |
| 5. | An novel support vector regression ( svr ) algorithm based on structural risk minimization inductive principle instead of empirical risk minimization principle was firstly introduced in well logs intelligent analysis
摘要基于核学习的支持向量机，是一种采用结构风险最小化原则代替传统经验风险最小化原则的新型统计学习方法，具有完备的理论基础。 |
| 6. | Support vector machine ( svm ) is a new method for pattern recognition based on the statistical learning theory . it is an implementation of structure risk minimization principle in the statistical learning theory
支持向量机( svm )是在统计学习理论基础上发展起来的一种新的模式识别方法，它是统计学习理论中的结构风险最小化思想在实际中的一种体现。 |
| 7. | Especially , the support vector machine ( svm ) text classification algorithm is discussed . we introduce the linear svm and the nonlinear svm and analyze the reason that svm is superior to other methods in theoretical
分类算法是文本分类的关键，介绍了线性支持向量机和非线性支持向量机，从结构风险最小化原则得到了支持向量机优于其它方法的结论。 |
| 8. | Statistical learning theory focuses on the rule of machine learning with small sample sets . support vector machine is a new generated machine learning technique based on vc dimension and structural risk minimization
统计学习理论是一种专门研究小样本情况下机器学习规律的理论,在统计学习的vc维理论和结构风险最小化原理的基础上,发展了支持向量机理论。 |
| 9. | A modified svm model , which can predict peak recognition theory , was proposed in this paper . this model can increase the weight of peak error in the loss function of structural risk minimization , thus improve prediction accuracy of hourly water demand peak
本文提出一种能够进行峰值识别的改进svm算法，该算法在结构风险最小化准则的目标函数中加大峰值误差的权重，从而提高时用水负荷峰值的预测精度。 |
| 10. | In this paper , a new method of signal de - noising is presented with three important factors being taken into consideration , that are mother functions , function order and the number of functions , and the proposed method is based on structural risk minimization ( srm ) and wavelet threshold method
摘要基于结构风险最小化方法将改进的小波变换用于故障信号消噪，它考虑的三个重要因素是：基函数，基函数排序和基函数个数选取。 |
