样本问题

Support Vector Machine is a novel learning method with solid theoretical basis depending on small amount of samples.

支持向量机是一种以坚实理论为基础的新的小样本学习方法，它避开了从9-3纳到演绎的传统过程，极大地简化了通常的分类和回9-3等问题。

In order to solve the difficult problem of the very small number of training samples in off-line signature verification, a new scheme based on the movement relativity of images in different coordinates of Euclidian space is proposed.

为了解决可用签名样本极少条件下的脱机签名鉴定问题，本文还提出了一种新的基于平面图像移动相对性原理建模的方案。

Aimed at the typical defects of sample resource lacking and easy local optimal convergence, in this paper, a new way of inserting ANN into genetic algorithm is implemented, which make full use of the nonlinear solution calculation ability of ANN and the full field optimal solution seeking ability of GA. The method optimized the tube stagger spinning parameters.

针对神经网络中样本资源紧张，易于收敛于局部优解的问题，本文将神经网络模型嵌入遗传算法中，充分利用了神经网络的非线性求解能力和遗传算法的全局寻优能力，实现了对错距旋压工艺参数的智能优化。

Statistical learning theory is a newly developed theory for studying the statistical estimation and prediction problem based on small number of samples.

统计学习理论是在研究小样本统计估计和预测的过程中发展起来的一种新兴理论，它试图从更本质上来研究机器学习问题，因此引起了人们越来越多的重视。

Without such detailed information, raking comes as a rescue since it requires only the knowledge of marginal distributions of selected variables. Popular as it may be, raking takes no account of associations among post-stratifying variables. Furthermore, it relies heavily on Chi-squared tests and a pre-selected p-value (usually 0.5) as the stopping rule of iteration, an ad hoc rule justified only by convenience.

因此，目前最常采用的是「反覆多重加权」的方式，但「反覆多重加权」实际执行时，最大的问题在於其检定方式是透过卡方检定，通常只要其检定P值大於0.5，就认定样本与母群一致，而未考虑一个最佳化的加权值。

The multi-index comprehensive evaluation model was established by artificial neural network BP algorithm. The evaluation index was described by adopting subordinative function of fuzzy mathematics. Sample-study pattern consists of the values of subordinative function on ends and on middle point.

利用人工神经网络BP算法建立了多指标综合评价模型，采用模糊数学的隶属函数对评价指标进行描述，由隶属函数的端点值和中间值组成学习样本模式，举例讨论了神经网络在多指标综合评价中的应用方法及应注意的一些问题。

In order to solve the problem of the invalidation of thermal parameters and optimal running,we present an efficient soft sensor approach based on sparse online Gaussian processes,which is based on a combination of a Bayesian online algorithm together with a sequential construction of a relevant subsample of the data to specify the prediction of the GP model.

为了解决电厂中热力参数失效和优化运行的问题，提出了一种基于稀疏高斯过程的软测量建模方法，它基于Bayes在线学习算法，通过构造序列的相关子样本来给出高斯过程的预测输出。

The system uses principal component analysis and BP neural networks to locate the suspectable target area, and then drives the related camera to capture the detail image around the target. In addition, it can solve the problem of redundant image data in the traditional machine vision system.

系统由多个低分辨率、低成本的摄像头获取不规则的图像，用主成分分析法对原始样本数据提取特征，然后由BP神经网络对特征进行分类识别以确定可疑区域位置，再控制从动摄像头获取目标区域的细节图像，解决了传统机器视觉系统固有的图像冗余数据问题。

Discussed the subject on the rich symbiont germplasm resource in China and on further research being necessary to exploitation and utilization of the benificial germplasm.

文摘：针对我国主要豆科作物大豆和花生的研究成果，并结合本身研究工作内容，(1)总结了与宿主共生的根瘤菌生物学多样性，包括大、小样本的土著根瘤菌群体数量、分离频率、类型与分布及其菌株―宿主共生混交性与亲和性；(2)评述了根瘤菌―宿主植物共生体双边固氮改良，包括优良菌株的选育、宿主品种资源共生特性的评价与利用及特异性状的选择；(3)讨论了我国这一类共生体资源的丰富性以及有必要进一步加大力度研究有益资源的开发和利用的问题。

To deal with the lack of fault samples in the fault detection of a Liquid Rocket Engine turbopump, a detection model based on one-class support vector machines was founded.

针对在火箭发动机涡轮泵的故障检测过程中缺乏故障样本的问题，应用单类支持向量机，为高速涡轮泵试车数据分析建立了一种新异类检测超球模型。

Do you know, i need you to come back

你知道吗，我需要你回来

Yang yinshu、Wang xiangsheng、Li decang,The first discovery of haemaphysalis conicinna.

1〕 杨银书，王祥生，李德昌。安徽省首次发现嗜群血蜱。