1) Minimum classification error training
最小分类误差训练
1.
Minimum classification error training method is used to incorporate the design of a linear transform matrixbased feature extractor and a classifier,which yields classification-oriented wavelet features and minimizes the error rate associate with the classifier.
在最小分类误差训练框架下,通过联合设计一个基于线性变换矩阵的特征提取器和一个分类器,来获取面向缺陷分类的小波框架特征,并最小化分类器的错误概率。
2) minimum classification error
最小分类误差
1.
Based on MultiBoost-a classification ensemble algorithm,this paper promotes the algorithm that can obtain the minimum classification error of MultiBoost by increment cross-validity so that under the specified size T of MultiBoost ensemble,it can find out the composite classifier with the minimum classification error.
基于MultiBoost分类组装技术,提出了一种用增量交叉验证技术求MultiBoost最小分类误差的算法,以使之在指定分类器数量T的范围内找出具有最小分类误差的合成分类器。
3) analysis of mean square error function
训练误差分解
4) training error
训练误差
1.
This paper taking one-hidden layer combined BP network model used in the fault diagnosis of power transformer as a exapmle and on the based of training flow process chart, points out that the number of hidden nodes, initial weight values, training error, the max training times and the order of training samples have different influence on the BP network generalization.
以单隐层的BP组合神经网络在基于DGA的电力变压器故障诊断中的应用为例,在BP网络训练流程图的基础上,分别举例阐述了隐层节点个数、初始权值、训练误差、最大训练次数以及训练样本次序对网络训练效果和泛化能力的影响。
2.
This theory shows that genetic algorithm,which uses minimum marginal as fitness function and where error is less than 50%,can have convergence influence on training error regarding two kinds of problems and have boundary on the general-ization error of testing integration.
该理论证明:在单个ANN具有一定的要求较低的性能条件下,即误差小于50%时,该ANN集成对两分类问题的训练误差具有收敛性,对测试集的泛化误差有界。
3.
This problem only causes a waste of neurons,but also results in the training error too great to meet the request of training precision and hardly completes the task of classification or clustering.
竞争型神经网络存在"死点"问题,某些神经元在竞争中可能始终未能获胜而成为"死神经元",不仅造成神经元的浪费,而且造成训练误差偏大,无法达到训练误差的精度要求,不能很好完成它所担负的聚类或分类任务。
5) the minimum error method
最小误差分割法
1.
First,to get rid of the repeated part between ordinal border upon images,image joint algorithm is adopted,and then,use a series of pretreating methods,such as gray transformation and image smoothing,to take off the yawp,segment the image with gray image binary transformation based on the minimum error method,by th.
首先,对顺序采集的胶球图像进行图像拼接,去除重复部分;然后采用灰度变换、图像平滑等一系列预处理算法去噪,去噪后结合基于最小误差分割法的灰度图二值化变换对图像进行分割,得到目标明确的分割图像;最后,利用二值图像投影变换,进行胶球位置定位,获得胶球的数量及磨损状况。
6) MCE
最小分类错误
1.
A Minimum Classification Error(MCE) criterion based sub-words weighting parameters estimation algorithm is proposed in which the sub-word weighting parameters are derived by the MCE training.
本文提出了一种基于最小分类错误准则(MCE)的子词权重参数估计算法,通过MCE训练得到子词的权重系数。
补充资料：最小距离分类
按照模式与各类代表样本的距离进行模式分类的一种统计识别方法。在这种方法中，被识别模式与所属模式类别样本的距离最小。假定c 个类别代表模式的特征向量用R1,...,Rc表示,x是被识别模式的特征向量，|x-Ri|是x与Ri(i＝1,2,...,c)之间的距离，如果|x-Ri|最小，则把x分为第i类。在更复杂的情况下可以用各类的代表样本集合，而不仅仅是用一个样本作为最小距离分类的基础(见近邻法分类)。进行最小距离分类首先要为每个类别确定它的代表模式的特征向量，这是用这种方法进行分类效果好坏的关键。各类代表特征向量可以根据所研究对象的物理、化学、生物等方面的机理来确定，常用的方法是收集各类样本，用各类样本特征向量的平均向量作为各类代表模式的特征向量。其次要选择一种确定的距离度量以计算被识别模式与各类代表模式特征向量之间的距离。常用的距离有欧几里得距离、绝对值距离等。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条