| 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. | Because of the lack of training samples , traditional methods based on experiential risk minimization can not play well in recognizing the characters
由于训练样本不足,决定了采用传统的基于经验风险最小化原则的识别方法难以取得较好的识别效果。 |
| 3. | 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
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。 |
| 4. | 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 。 |
| 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. | 1 . a modified denoising method based on vc dimension and wavelet package is presented , improving the shortcomings of denoising methods based on empirical risk minimization and wavelets thresholds
针对传统的基于经验风险最小化信号消噪方法和现有的小波阈值信号消噪方法的不足，基于统计学习理论，提出了一种改进的vc维小波包信号消噪方法。 |
| 7. | 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。 |
| 8. | 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。 |
| 9. | 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。 |
| 10. | 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。 |
