When solving this small sample problem with high dimension and nonlinear, many traditional pattern recognition methods will tend to occur overfitting phenomenon.
对于这样一个高维数、非线性的小样本问题，许多传统的模式识别方法都容易出现过学习或欠学习现象。
Absrtact: SVM represented many unique advantages in many applications, such as solving the problem of nonlinear, high dimension pattern recognition and small sample problem.
摘要：在解决非线性、高维模式识别以及小样本等问题中，支持向量机表现出许多独有的优势。
SVM solves the small sample problem mainly and finds the best compromise between the complexity of the model and the learning ability in order to obtaining the best generalization ability.
SVM主要解决小样本问题，在模型的复杂度和学习能力之间寻求最佳折衷，目的在于获得最好的泛化能力。
A novel algorithm for solving the small sample size problem in face recognition is proposed.
提出了一种在人脸识别中解决小样本问题的新算法。
This is due to SVM can solve the small sample, nonlinear classification problem, which is the characteristics of the latent radar fault.
这是由于SVM可以很好地解决小样本、非线性分类问题，而这正是潜伏性雷达故障的特点。
The algorithm combines active learning, biased classification and incremental learning to model the small sample biased learning problem in relevance feedback process.
该算法将主动式学习、有偏分类和增量学习结合起来，对相关反馈过程中的小样本有偏学习问题进行建模。
Because of its strict mathematical theory of support and good generalization performance, it addresses the problem of small sample study of particular advantage.
由于它有严格的数学理论支撑以及较强的泛化性能，它在解决小样本学习问题时尤其具有优势。
Experiments demonstrate that the proposed method can effectively solve the small sample size problem of LDA.
多个人脸数据库上的实验结果表明，本算法能够有效地解决线性判别分析中的小样本规模问题。
As a new machine learning method, SVM can solve the small sample, nonlinear, high dimension and local minima, the actual problem.
作为一种新的机器学习方法，SV M能较好地解决小样本、非线性、高维数和局部极小点等实际问题。
Statistical Learning Theory is based on a solid theoretical foundation. It provides an unified framework for solving the small sample learning problem.
统计学习理论具有坚实的理论基础，为解决小样本学习问题提供了统一的框架。
During analyzing and estimating the risk, we often meet with the situation of inadequate sample information and incomplete data, that is, small-sample problem.
在进行风险分析和评估过程中，经常遇到样本信息不充分，数据不完备，即小样本问题。
Face recognition is essentially a typical small-sample pattern recognition problem in sparse hyper-high dimensional space.
人脸识别实质是稀疏超高维空间、典型的小样本模式识别问题。
To solve the problem of lack of fault engine sample, support vector machines, which is a method based on small sample theory is applied.
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。
Through maximalizing the margin, we can obtain the optimal projection vector, and avoid the small sample size problem due to singularity of the within-class scatter.
通过极大化该边界获得最优投影向量，同时避免因类内离散度矩阵奇异导致的小样本问题。
Because of nonlinear effect and small calibration sample set in fact, it is important to solve the problem of model transfer under the condition of nonlinear effect in evidence and small sample set.
由于实际测量数据具有非线性特征，加上校正样本集合的有限性，使得解决小样本条件下非线性关系的模型传递问题显得尤为重要。
However, support vector machine (SVM) can better solve problem of small-sample learning and provides the foundation for solving intelligent diagnosis problems.
而支持向量机能够较好地解决小样本学习问题，为解决智能诊断的这一问题提供了基础。
Support vector machine is a new technique of data mining, which is regarded as the best theory aimed at solving the problem of classification and regression of small sample pool at present.
支持向量机是数据挖掘的一项新技术，被认为是目前针对小样本的分类、回归等问题的最佳理论。
In ORL face database, the experimental results prove that the algorithm outperforms traditional methods in small sample size problem.
在OR L人脸库上的实验结果说明，该算法对小样本数据的识别具有明显优势。
Compared with traditional method, this presented method not only has higher precision but also solves the problem of reliability assessment with very small sample.
与传统方法相比，该方法具有信息量大，精度高的特点，能够进行极小子样可靠性评定。
Compared with traditional method, this presented method not only has higher precision but also solves the problem of reliability assessment with very small sample.
与传统方法相比，该方法具有信息量大，精度高的特点，能够进行极小子样可靠性评定。
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