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。
An improved design method on pattern classifier based on multi-layer perceptrons (MLP) by means of minimum classification error (MCE) training was proposed.
提出了一种基于最小分类错误(MCE)训练的采用多层感知器(MLP)结构的模式分类器设计方法。
The experiment based on UCI data sets proves the algorithm can obtain a faster training rate and higher classification accuracy.
随后的基于UCI数据集的实验结果表明，该算法获得较快的训练速率和较高的分类精度。
Thirdly, a GPU based massively data parallel C-SVM classification (GMP-CSVC) algorithm is presented to reduce the training time of SVM.
第三，针对支持向量机算法复杂度较高，难以应用于大样本分类的问题，提出了GMP-CSVC算法。
A method based on Riemannian metric to the classification problem with imbalanced training data was proposed.
本文提出一种基于黎曼度量的训练样本类不平衡问题的分类方法。
When training sets with uneven class sizes are used, the classification error based on C-Support Vector Machine is undesirably biased towards the class with fewer samples in the training set.
SVM分类算法在不同类别样本数目不均衡的情况下，训练时的分类错误倾向于样本数目小的类别。样本集中出现重复样本时作为新样本重新计算，增加了算法的训练时间。
For text classification based on SVM learning algorithm, usually there is an abundance of training data, which will cost a lot of computing resources in training process.
在采用SVM算法的文本分类中，由于文本所表征的向量空间维数通常非常巨大，因此在训练过程中将耗费大量的系统资源。
Adaptive algorithm of speech detection based on statistical classification is presented to reduce the dependence of the training data.
该文针对统计分类语音算法对训练数据的依赖问题，提出自适应算法在线动态更新分类模型。
Adaptive algorithm of speech detection based on statistical classification is presented to reduce the dependence of the training data.
该文针对统计分类语音算法对训练数据的依赖问题，提出自适应算法在线动态更新分类模型。
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