试验结果表明该方法是有效、可行的，且在小样本情况下比BP神经网络具有更高的诊断精度。
The test shows that this method is effective and feasible, and has higher diagnosis precision than BP neural network in cases of fewer samples.
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.
提出一种在小样本的情况下，基于多层贝叶斯网络的医学图像语义建模方法。
A semantic modeling approach for medical image semantic retrieval based on hierarchical Bayesian networks was proposed, in a small set of samples.
在某些情况下，医生可能会想看看一个小样本的心脏组织，称为一个切片检查，以明确诊断。
In some cases, a doctor may want to look at a small sample of heart tissue, called a biopsy, to make a definite diagnosis.
在罕见的情况下，一个小样本发炎的皮肤可能会采取的。
In rare cases, a small sample of inflamed skin may be taken.
由于使用结构风险最小化原则代替经验风险最小化原则，使它能较好地处理小样本情况下的学习问题。
The main advantage of SVM is that it can serve better in the processing of small-sample learning problems by the replacement of Experiential Risk Minimization by Structural Risk Minimization.
仿真结果表明，最小二乘支持向量机降低了计算复杂度，且有较快计算速度，在小样本情况下具有良好的泛化能力；
Simulation results show that LS-SVM reduces calculating complexity with high calculating speed. And it also has good generalization ability with small sample.
在小样本的情况下，局部矩估计可以给出参数较理想的区间估计。
Second, the local moment estimation may give the more ideal interval estimation to the parameter under small sampling condition.
目的：探讨建立小样本情况下病例组合模型的意义与模型求解方法。
Objective: To investigate the methods of case-mix in small samples.
实验表明，算法在小样本情况下对于两类和多类问题均具有良好的推广性能，优于多种线性判别准则的改进算法； 并且在样本较多时也取得了满意结果。
The experiment results show that MFE outperforms several improved versions of LDA in the case of small samples, and achieve a satisfying performance for larger samples.
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.
支持向量机是一种适用于小样本情况下的故障诊断和质量预测的有效方法。
Support Vector Machines(SVM) is an effective method applicable to the fault diagnosis and quality prediction when the samples are small.
但是在小样本情况下，精确度会有很明显的降低，同时，当样本量改变时，同一样本的识别结果可能会不一致。
However in the small sample case the accuracy rate may drop significantly and the calling results are not consistent as the sample size changes.
实验结果表明，该算法能够对人脸图像进行良好的分类识别，尤其是小样本的情况下。
The experimental results show that this algorithm is able to face a good image classification and recognition, especially in the case of small samples.
该算法利用预测误差阈值进行样本的取舍，在尽量保留有用信息的情况下减小样本训练规模。
This algorithm USES the prediction error threshold to retain the useful information to decrease sample training scale.
目的：探讨建立小样本情况下病例组合模型的意义与模型求解方法。
Objective:The aim of this study is to provide method reference for the research of in-patient expenses based on casemix.
在小样本观测数据情况下，研究利用日地月方位信息和日月星历表进行航天器自主导航以及利用DSP实现航天器自主导航器的技术。
The orientation information of the sun, the moon and the earth, together with ephemeris are utilized to develop autonomous navigation algorithm, as well as its realization by DSP hardware.
这主要是由于支持向量机目标是在小样本情况下追求学习的最优性能，更加符合实际情况，从而在现实中具有更好的推广能力。
SVM usually has a better performance than the conventional methods because it is to acquire the best function when the number of training data is small which is often met in true-life.
这主要是由于支持向量机目标是在小样本情况下追求学习的最优性能，更加符合实际情况，从而在现实中具有更好的推广能力。
SVM usually has a better performance than the conventional methods because it is to acquire the best function when the number of training data is small which is often met in true-life.
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