支持向量机(SVM)是一种新的通用学习机器，它从结构风险最小化的角度，分析了学习过程的一致性、收敛速度等。

Support vector machine (SVM) is a new general learning machine, which analyzes the consistency of learning and speed of convergence from structure risk minimization principle.

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本文用支持矢量学习机和神经网络两种机器学习方法建立选择性环氧化酶-2抑制剂的活性预测模型，以期为选择性环氧化酶-2抑制剂药物的合成提供先导化合物。

Machine learning methods, including Support Vector Machines and Artificial Neural Network, are applied to the development of the classification models for the selective COX-2 inhibitors in this paper.

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基于样本的机器学习，要求学习机在容量控制和过拟合之间取一个折衷，从而保证学习机的推广能力和误差精度。

A differential method for capacity control is presented based on the analysis of the capacity of learning machines.

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支持向量机(SVM)是一种新的机器学习机制。

Support vector machine (SVM) is a new mechanism of machine learning.

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支持向量机(SVM)是一种新的机器学习机制。

Support vector machine (SVM) is a new mechanism of machine learning.

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