This paper firstly investigates the application of support vector machines for medical image classification.

本文首次采用支持向量机方法对医学图像进行了分类研究。

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Support vector machines (SVM) are a kind of novel machine learning methods, based on statistical learning theory, which have been developed for solving classification and regression problems.

支持向量机是一种基于统计学习理论的新颖的机器学习方法，该方法已广泛用于解决分类和回归问题。

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The paper designed a new histogram kernel function for support vector machines which achieved good results in image classification .

说明：为支持向量机设计了一种新的直方图核函数运用在图像分类上取得不错的效果。

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Support vector machines (SVMs) which suit to classification problem for tiny samples is designed for different shot types through the features extracted by the method.

利用所提出的特征，采用适合小样本分类问题的支持向量机(SVM)对足球视频镜头分类。

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A hierarchical decomposed support vector machines binary decision tree is used for classification.

采用一种层次分解的支持向量机二叉决策树进行分类识别。

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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.

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。

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Support Vector Machines(SVM) are developed from the theory of limited samples Statistical Learning Theory (SLT) by Vapnik et al. , which are originally designed for binary classification.

支持向量机（SVM）是建立在统计学习理论基础上的一种小样本机器学习方法，用于解决二分类问题。

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This paper proposes an approach for cancer molecular classification using support vector machines.

针对该类问题，论文提出了一种利用支持向量机进行肿瘤分类与判别的方法。

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This paper proposes an approach for cancer molecular classification using support vector machines.

针对该类问题，论文提出了一种利用支持向量机进行肿瘤分类与判别的方法。

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