提出了一种在人脸识别中解决小样本问题的新算法。
A novel algorithm for solving the small sample size problem in face recognition is proposed.
第二种为灰色系统理论方法，用此方法能解决实测地应力值小样本问题。
The second one is a method using gray system theory, with which problem of a few samples of measured in-situ stress value could be resolved.
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。
During analyzing and estimating the risk, we often meet with the situation of inadequate sample information and incomplete data, that is, small-sample problem.
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。
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.
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。
When solving this small sample problem with high dimension and nonlinear, many traditional pattern recognition methods will tend to occur overfitting phenomenon.
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。
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.
通过在个体类内保局差异散度矩阵的零空间中求最优特征向量，避免了矩阵的奇异性问题，解决了小样本问题。
The optimal feature vectors are extracted from the null space of intrapersonal locality preserving difference scatter matrix, which avoids the singularity and the SSS problem is solved.
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。
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.
该方法为小样本、贫信息的科研项目风险的评价问题提供了可行的数学分析工具。
The method provides a tool of analysis on risk evaluation for such projects with limited number of samples and amount of information.
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。
This is due to SVM can solve the small sample, nonlinear classification problem, which is the characteristics of the latent radar fault.
支持向量机(SVM)作为一种新型的非线性建模方法，适合于处理小样本和高维数的建模问题。
Support vector machines (SVM) is a new nonlinear modeling method which is suitable for solving small samples and high dimension modeling problems.
支持向量机因其适用高维特征、小样本与不确定性问题的优越性，是一种极具潜力的高光谱遥感分类方法。
Support Vector Machines(SVM) is a potential hyperspectral remote sensing classification method because it is advantageous to deal with problems with high dimensions, small samples and uncertainty.
支持向量机方法能够解决小样本情况下非线性函数拟合的通用性和推广性的问题，是求复杂的非线性拟合函数的一种非常有效的技术。
The problems of universality and extensibility in nonlinear function approximation using small samples can be solved by the method, it a very efficient technique for nonlinear function approximation.
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。
The algorithm combines active learning, biased classification and incremental learning to model the small sample biased learning problem in relevance feedback process.
利用所提出的特征，采用适合小样本分类问题的支持向量机(SVM)对足球视频镜头分类。
Support vector machines (SVMs) which suit to classification problem for tiny samples is designed for different shot types through the features extracted by the method.
经过实验证明支持向量机在解决小样本、非线性问题中表现出很好的优势。
Experimental results indicate that the support vector machine performs a number of unique advantages in solving the small sample size, non-linear problems.
考虑一类整数规划问题，其诸局势效益值时序相互关联，且呈现小样本无明显统计特征。
This paper discusses a kind of integer planning problems, whose situation benefit values are some relational time series with little samples, and are not typical statistic properties.
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。
Because of its strict mathematical theory of support and good generalization performance, it addresses the problem of small sample study of particular advantage.
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。
Experiments demonstrate that the proposed method can effectively solve the small sample size problem of LDA.
同时针对神经网络易于陷入局部极值、结构难以确定和泛化能力较差的缺点，引入了能很好解决小样本、非线性和高维数问题的支持向量回归机来进行油气田开发指标的预测；
The method of support vector regression which can well resolve the problem with the insufficient swatch, nonlinear and high dimension is introduction to predict the development index of gas-field.
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。
Statistical Learning Theory is based on a solid theoretical foundation. It provides an unified framework for solving the small sample learning problem.
实际数据处理结果表明，该方法在小样本情况下性能优于神经网络，可以很好地克服过学习问题。
The result of practical application indicates that the performance of SVM has superiority over ANN and can overcome the problem of "over fitting" excellently.
本文在中、小样本试验数据下，研究响应模型的选择问题。
Binary response model choice is researched with the data of media or small sample size.
为了解决支持向量机的分类仅应用于较小样本集的问题，提出了一种密度聚类与支持向量机相结合的分类算法。
To solve the problem that support vector machine(SVM) can only classify the small samples set, a new algorithm which applied SVM to density clustering is proposed.
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。
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.
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。
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.
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。
Face recognition is essentially a typical small-sample pattern recognition problem in sparse hyper-high dimensional space.
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。
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。
However, support vector machine (SVM) can better solve problem of small-sample learning and provides the foundation for solving intelligent diagnosis problems.
应用推荐