• It operates on a principle, called structural risk minimization, which aims to minimize the upper bound on the expected generalization error.

    它基于结构风险最小化准则,目的是最小化泛化误差上界。

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  • Subspace information criterion is a new criterion for model selection, it gives an unbiased estimate for the generalization error under some assumptions.

    指出子空间信息准则是模型选择的一种新准则,它在一些假设条件下,给出推广误差的一种无偏估计。

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  • The generalization error of Support Vector Machine is approximately equal to that of Probabilistic Neural Network. And Support Vector Machine is easier to use than Neural Networks.

    支持向量机的分类误差与概率神经网络相近,但支持向量机的使用较概率神经网络简单。

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  • In essence, deferreds are a generalization of scheduled callbacks, but allow techniques such as chaining dependent callbacks and handling error conditions in these chains.

    实际上,延迟是对象被调度回调的泛化,但它们也允许使用诸如链接依赖回调和在这些链接中进行错误条件处理之类的技术。

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  • The effluent treatment process was predicted with this BP neural network model with the average relative error of 19%, which indicates that the generalization power of the network is not so desirable.

    利用BP神经网络模型实现了对造纸废水处理过程的预测,平均相对误差为19%,表明网络泛化能力不是很好。

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  • The root mean square relative error, mean absolute relative error and maximize absolute relative error of SVM model generalization performance are 1.06%, 0.96% and 1.16%, respectively.

    对SVM多元非线性回归泛化性能进行测试,其均方根相对误差为1.06%,平均绝对相对误差为0.96%,最大绝对相对误差为1.16%。

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  • So adding a penalty term to the error function of BP network is an important approach to gain better generalization.

    所以在BP网络的误差函数中加入惩罚项是提高网络泛化能力的一个重要途径。

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  • At last the proposed network is used to the construction bidding system. The results of the simulation indicate that it can obtain higher error precision, training speed and generalization ability.

    最后将提出的网络结构应用于建筑工程的投标报价中,仿真结果证明:该网络能达到更高的误差精度、更快的训练速度和更好的泛化能力。

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  • After speed of statistical convergence and MSE (Mean Square Error) of these algorithms are compared, Bayes Generalization algorithm is assessed to be the most appropriate.

    在比较收敛速度和均方差后,确定选用贝叶斯正则算法建立模型。

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  • After speed of statistical convergence and MSE (Mean Square Error) of these algorithms are compared, Bayes Generalization algorithm is assessed to be the most appropriate.

    在比较收敛速度和均方差后,确定选用贝叶斯正则算法建立模型。

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