| 1. | A much greater structural risk occurs in temperate and tropical areas .
在温带和热带地区会出现更大的结构风险。 |
| 2. | The structural risks and the construction of knowledge society
结构性风险与知识社会的建构 |
| 3. | Structural risks challenging china ' s foreign trade in terms of degree of dependence on foreign trade
从外贸依存度看中国外贸面临的结构型风险 |
| 4. | 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。 |
| 5. | 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。 |
| 6. | Based on analysis of the conclusions in the statistical learning theory , especially the structural risk minimization and the - insensitive loss function , a novel linear programming support vector regression is proposed
摘要通过对统计学习理论中的支持向量回归问题，特别是结构风险问题和-不敏感函数的分析，得到了一种新的支持向量回归算法。 |
| 7. | 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。 |
| 8. | Support vector machines ( svm ) are a kind of novel machine learning methods . it can solve small - sample learning problems better by using experiential risk minimization in place of structural risk minimination
支持向量机( supportvectormachines ，简称svm )是在统计学习理论的基础上发展起来的一种新的学习方法，它已初步表现出很多优于已有方法的性能。 |
| 9. | 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。 |
| 10. | 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。 |
