This sort of learning could take place with neural networks or support vector machines, but another approach is to use decision trees.
这种学习可以使用神经网络或者支持向量机，不过用决策树也可以实现类似的功能。
Extensive comparisons have shown that support vector machines obtain good generalization performance on various data sets.
大量的对比实验已经表明支持向量机在很多问题上获得了良好的推广能力。
Support vector machines is a new statistical learning method based on structural risk minimization principle, and it has integrated theory and valid learning generalization ability.
支持向量机是一种基于结构风险最小原则的新型机器学习方法，具有完备的理论依据和良好的学习泛化能力。
Support vector machines (SVM) are a kind of novel machine learning methods based on statistical learning theory, which has been developed to solve 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。
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。
Support vector machines based on statistical learning theory is a typical example.
基于统计学习理论的支持向量机便是一个典型的例子。
The support vector machines(SVM) model with multi-input and single output was proposed.
建立了多输入、单输出的支持向量机（SVM）预测模型。
Then support vector machines using the kernel are trained to perform image classification.
然后将该核函数应用于支持向量机，对图像进行分类。
An incremental learning algorithm using multiple support vector machines (SVMs) is proposed.
给出了使用多支持向量机进行增量学习的算法。
Based on the theory and technique of the support vector machines, an assessment system was built.
基于支持向量机的理论和技术，构建了换档质量评价系统。
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。
A new geometric fast incremental learning algorithm for support vector machines (SVM) was proposed.
提出了一种新的基于壳向量的增量式支持向量机快速学习算法。
In this paper we present an improved dimensionality reduction method based on support vector machines.
提出了一种基于支持向量机的改进的降维方法。
It is a very challenging work to deal with large regression problems based on support vector machines.
基于支持向量机的大样本回归问题一直是一个非常具有挑战性的课题。
Based on the theory and technique of the support vector machines (SVMs), an assessment system was built.
基于支持向量机的理论和技术，构建了换档质量评价系统。
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。
However, the training procedure of support vector machines amounts to solving a constrained quadratic programming.
然而，支持向量机的训练过程等价于求解一个约束凸二次规划。
The SVM (support vector machines) is a classification technique based on the structural risk minimization principle.
是一种基于结构风险最小化原理的分类技术。
Experiment results show that the least squares support vector machines method has high recognition rate and is practical.
实验结果表明此方法的识别率较高，在小字符集识别中具有较强的实用性。
The paper presents a method of pose-varied face recognition based on neural network and hierarchical support vector machines.
提出了一种基于神经网络和层次支持向量机的多姿态人脸识别方法。
In this paper a method of face recognition based on clustering algorithm and hierarchical support vector machines is presented.
提出一种基于聚类算法和层次支持向量机的人脸识别方法。
A face detection method based on a hierarchical support vector machines (SVM) presents improved methods for both of these problems.
提出了一种基于层次型支持向量机的正面直立人脸检测方法，在这两方面作了改进。
This paper presents an application of least squares support vector machines in small-set pressed protuberant character recognition.
将最小二乘支持向量机引入到小字符集压印字符识别中。
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。
Transductive support vector machines (TSVM) classifies the new data vector based on the information only related to this data vector.
直推式支持向量机(TSVM)是一种直接从已知样本出发对特定的未知样本进行识别和分类的技术。
To solve the problem of lack of fault engine sample, support vector machines, which is a method based on small sample theory is applied.
针对这一缺陷，将基于小样本理论的支持向量机学习方法应用到发动机的故障诊断中。
Theory analysis and primary numerical results illustrate that maximum entropy method for support vector machines is feasible and effective.
该方法特别易于计算机实现，理论分析和数值实验结果表明了模型和算法的可行性和有效性。
In order to realize hydraulic pump's auto fault early warning, a new method based on online one class support vector machines was presented.
为了实现液压泵自动故障预警，提出了一种基于在线单类支持向量机的新方法。
Support vector machines (SVM) is a new nonlinear modeling method which is suitable for solving small samples and high dimension modeling problems.
支持向量机(SVM)作为一种新型的非线性建模方法，适合于处理小样本和高维数的建模问题。
Support vector machines (SVM) is a new nonlinear modeling method which is suitable for solving small samples and high dimension modeling problems.
支持向量机(SVM)作为一种新型的非线性建模方法，适合于处理小样本和高维数的建模问题。
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