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small training set     
相关语句
  小样本
     The Method of Building a Skin Model Based on Small Training Set
     基于小样本训练集的肤色模型建立方法
短句来源
     Since SVM can capture geometric characteristics of feature space and capable of extracting the optimal solution with the small training set size,the classifier of SVM approach outperforms BPN to the problem of corporate bankruptcy prediction.
     由于SVM能够使用小样本捕获特征空间的几何特征并抽取出最优解,因此对于企业破产预测问题,使用SVM方法构建的分类机的性能比倒传递神经网络模型(BPN)方法构建的分类机的性能要好。
短句来源
     A new recognition method, Support Vector Machine (SVM) was presented, which has excellent learning and generalization ability in solving learning problem with small training set of sample.
     提出了一种新的模式识别方法—支持向量机方法在处理小样本问题时具有很好的学习能力和推广性。
短句来源
  小训练集
     A text classification method for small training set is provided.
     提出了一种针对小训练集环境的文本自动分类方法。
短句来源
  小的训练集
     With a small training set and a simple network architecture,a high prediction accuracy has been achieved,i. e. ,self-consistence accuracy 97.62%,jack-knife test accuracy 97.62% and extrapolating accuracy 90.74% on average.
     仅使用了一个小的训练集和简单的网络结构 ,获得了很高的预测精度 :自支持精度 97 6 2 % ,jack -knife测试精度 97 6 2 % ,及平均外推精度 90 74 %。
短句来源
  小样本训练集
     The Method of Building a Skin Model Based on Small Training Set
     基于小样本训练集的肤色模型建立方法
短句来源

 

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      small training set
    However, data accumulated in manufacturing plants have unique characteristics, such as unbalanced distribution of the target attribute, and a small training set relative to the number of input features.
          
    We demonstrate its suitability for high-dimensional problems with small training set sizes.
          
    Using the small training set, we generated several population of 5 up to 10 MLPs.
          
    The small training set size is selected as an exercise in exploration intended to push the limits of generalization.
          
    The one outlier is family 2.44.1.2, which has a relatively small training set.
          
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    The statistical-fuzzy method and the fuzzy-statistical method for pattern recognition are developed in this paper on the bas's of the discussion on the generalities, differences and the respective suitable scopes of statistical approaches and fuzzy approaches to puttern recognition. The statistical-fuzzy method is to adopt in a fuzzy classifier the membership functions which make full use of the statistical information of the pattern components, so that the performance of the classifier is better than that of...

    The statistical-fuzzy method and the fuzzy-statistical method for pattern recognition are developed in this paper on the bas's of the discussion on the generalities, differences and the respective suitable scopes of statistical approaches and fuzzy approaches to puttern recognition. The statistical-fuzzy method is to adopt in a fuzzy classifier the membership functions which make full use of the statistical information of the pattern components, so that the performance of the classifier is better than that of common fuzzy classifiers. The fuzzy-statistical method is to replace the pattern components by their fuzzy membership functions as inputs in a. classifier which is based on the statistical method. From the results of the classification experiments made with the data sets given in this paper, it can be seen that the classification performance of this method can approach the optimal level of the Bayesian classifier with quite a small training set.

    本文在论述模式识别的统计方法和模糊方法的共同性、差异以及各自适用范围的基础上,研究了模式识别的统计模糊方法和模糊统计方法。统计模糊方法是在模糊分类器中充分利用模式分量统计信息的隶属函数,使分类性能优于普通的模糊分类器。模糊统计方法是在以统计方法为基础的分类器中,用模式分量的模糊隶属函数代替模式分量作为分类器输入。从对本文中几个数据集所作的分类试验结果看,这种方法只需要不大的训练样本集便可使分类性能接近于Bayes分类器的最佳水平。

    This work presents a backpropagation neural network trained to reproduce the reaction yield of aryl fluorides by the halex technique. The work shows that a ten\|dimensional input space is able to reproduce reasonably the observed reaction yields by employing statistics in artificial neural system. By means of a number of multilayer feedforward (MLF) networks rather than one, the disadvantages caused by network randomness are limited greatly, and therefore the prediction quality is improved. The combined approach...

    This work presents a backpropagation neural network trained to reproduce the reaction yield of aryl fluorides by the halex technique. The work shows that a ten\|dimensional input space is able to reproduce reasonably the observed reaction yields by employing statistics in artificial neural system. By means of a number of multilayer feedforward (MLF) networks rather than one, the disadvantages caused by network randomness are limited greatly, and therefore the prediction quality is improved. The combined approach is suitable for relatively small training set, which often causes overfitting and leads to unreliable prediction results.

    在应用人工神经网络预测有机反应产率中 ,由于结合了统计方法 ,使人工神经网络易产生的随机性和过拟合作用造成的不利影响减小 ,从而提高了预测可靠性 .

    The technology of support vector machines is being used to solve problems of pattern recognition. Posteriori probability of samples is important in pattern recognition. But standard support vector machines do not provide posteriori probability. Discussed below are several questions based upon posteriori probability in the support vector machine: (1) decomposing the nonlinear optimal problem of a large training sample set into two nonlinear optimal problems of small training set; (2) designing the algorithm...

    The technology of support vector machines is being used to solve problems of pattern recognition. Posteriori probability of samples is important in pattern recognition. But standard support vector machines do not provide posteriori probability. Discussed below are several questions based upon posteriori probability in the support vector machine: (1) decomposing the nonlinear optimal problem of a large training sample set into two nonlinear optimal problems of small training set; (2) designing the algorithm to revise the traditional optimal hyperplane, and analyzing the rationality of the algorithm; and (3) showing the results from testing on three image data sets effectively.

    目前支持向量机解决模式识别问题是广大学者研究的热点,样本的后验概率在模式识别中至关重要,但是传统的支持向量机技术不提供后验概率.针对这一问题进行了3个方面的研究:①在给出样本点后验概率的基础上,将大规模优化问题分解成最大似然函数和最大分类边界两个小规模优化问题;②给出了一种新的用后验概率修正最优分离超平面的方法,并且分析了该新方法的合理性;③用图像分类的3组实例说明本方法的有效性.

     
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