The theory of Support Vector Machines is presented.
阐述了模糊支持向量机的原理。
Tutorial on Support Vector Machines for Pattern Recognition.
一本关于支持矢量机的英文入门书籍。
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。
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。
So Support Vector Machines is introduced in the Automatic Grading of Tobacco Leaf in the paper.
在此，本文将支持向量机技术引入到烟叶自动分级中。
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。
Thus we put the theory and method of Support Vector Machines to apply partner selection in this thesis.
因而，本文的另一项主要工作是将支持向量机的理论和方法引入盟友选择领域。
Flight action recognition is proposed in this paper based on the fuzzy support vector Machines (FSVMs).
针对飞行动作识别提出解决这一现象的模糊支持向量机。
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。
A method of the electro hydraulic servo system identification modeling is presented based on support vector machines.
提出了电液伺服系统的支持向量机的辨识建模方法。
In particular methods for confidence estimation and feature selection with Support Vector Machines will be described.
特别是支持向量机的特征选取和信赖度估计方法。
Support Vector Machines algorithm is a simple and effective classification method based upon statistical learning theory.
支持向量机是基于统计学习理论框架下的一种简单、有效的分类方法。
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。
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。
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。
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。
In this paper, we provide a new human face detection algorithm based on template-matching, Mosaic image and Support Vector Machines.
本文提出了一种基于模板匹配、马赛克图和支持向量机的人脸检测算法。
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。
This class, taught by the guest speaker, is devoted to the optimization problems underlying the implementation of Support Vector Machines.
这堂课将由授课的特别来宾讲解构成向量机执行基本原理的最佳化问题运用。
The paper presents a method of Chinese chunk recognition based on Support Vector Machines (SVM) and transformation-based error-driven learning.
本文研究了一种支持向量机(SVM)和基于转换的错误驱动学习相结合的汉语组块识别方法。
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。
应用推荐