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.
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。
Since the data samples in machine learning and pattern recognition problems generally distribute in multi-modal distribution, this thesis proposed a prototype based feature ranking model.
由于模式识别、机器学习等问题的复杂性比较高，数据分布通常呈现多模态分布。
This was further demonstrated with the success of their computer learning models in being able to identify each participant based solely on their samples.
这进一步证实了他们仅凭参与者样本就能识别出每个参与者的计算机学习模型是成功的。
As one algorithm of the machine learning based on the statistical learning theory, Support Vector machine (SVM) is specifically to the small samples learning case.
支持向量机是一种基于统计学习理论的机器学习算法，能够较好地解决小样本的学习问题。
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。
There are usually few training samples in the tasks of content-based remote sensing image retrieval, which will lead to over-learning problem while using this small data set for training.
提出一种基于多分类器协同训练的遥感图像检索方法，该方法在不同特征集上分别建立分类器，利用不同分类器的协同性自动标记未知样本，从而有效解决了小样本问题。
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.
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。
Finally the key theorem of statistical learning theory based on random rough samples is proved, and the bounds on the rate of uniform convergence of learning process are discussed.
最后证明基于双重随机样本的统计学习理论的关键定理并讨论学习过程一致收敛速度的界。
The PNN structure was optimized based on statistical results from the PCA for the training samples. A learning algorithm was introduced into the PNN to reduce uncertainties parameter.
以概率乘法公式为理论依据，根据训练样本的PCA结果对PNN进行结构优化，并引入学习算法减小PNN的参数不确定性。
The PNN structure was optimized based on statistical results from the PCA for the training samples. A learning algorithm was introduced into the PNN to reduce uncertainties parameter.
以概率乘法公式为理论依据，根据训练样本的PCA结果对PNN进行结构优化，并引入学习算法减小PNN的参数不确定性。
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