大部分数字识别的工作可以由神经网络来完成，但最近支持向量机也被证明可以在分类方面做得更好。
Much of the work on digit recognition has been done in the neural network community, but more recently support vector machines have proven to be even better classifiers.
在诊断模型中，应用APEX网络提取分类信息，压缩向量空间维数，利用前馈网络建立其类型识别函数。
In the model, using APEX network extracts classification information and condenses vector space dimensions, making use of feedforward network establishes the classification recognition function.
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。
This paper firstly investigates the application of support vector machines for medical image classification.
本文主要研究目前较为流行的基于统计学习理论的分类方法——支持向量机方法(SVM)，以及小波变换提取特征的方法，将其用于人脸检测。
This paper mainly make research on classify methods based on statistical theory, support vector machine (SVM), and feature extraction method-wavelet transform, and using them in human face detection.
采用小波多分辩率分析方法提取基因表达的特征，利用支持向量机和BP神经网络方法进行分类。
The features of gene expression are extracted by the wavelet multi-resolution analysis, the features are classified by the support vector machines and BP neural network methods.
目的：探讨带先验知识的支持向量机（P-SVM）数据挖掘算法在中医证候信息自动分类中的应用。
The paper explores possible applications of Prior knowledge Support Vector Machine (P-SVM) based data mining algorithm in an automatic TCM syndrome classification system.
论文将支持向量机引入到动态电能质量分类问题中。
This paper presents a Support Vector Machine (SVM) method for classification of dynamic power quality disturbances.
在此基础上，研究了用球结构支持向量机作分类器，对滚动轴承内圈故障的劣化程度进行识别的理论和方法。
Basing on these, study theory and method of using Sphere-structured Support Vector Machines to recognize the roll bearing inside track's fault deterioration extent.
特征提取的依据是利用有关数学工具，去掉对分类无用的信息，寻找最有效的信号特征来构成用于分类识别的模式特征向量。
The extracting process is to throw off the useless information and look for the most efficient signal feature to form a pattern feature vectors for classification with mathematics tools.
首先利用色度矩提取植物病害叶片的特征向量，然后利用支持向量机分类方法进行病害的识别。
At first extracting features of chromaticity moments was done then classification method of SVM for recognition of plant disease was discussed.
论文将支持向量机的机器学习方法引入到医学图像的分类问题。
In this thesis, SVM as a new machine learning method is brought into medical image classification.
然后基于支持向量机进行分类建模和预测过程。
Then, we implement classification modeling and forecast based on SVM.
该文介绍了一种文本过滤算法，该算法把基于空间向量模型的主题分类算法与基于支持向量机文本态度分类结合起来。
This paper introduces a text filtering system merging topic classification based on vector space model and sentiment classification based on support vector machine.
提出了基于支持向量机的分类原理对鸢尾属植物进行分类的方法。
This article presents a new method of plant classification of iris based on the support vector machine classification principle.
实验结果表明，采用支持向量机方法进行变异语音分类是可行的。
Experimental results indicate that it is feasible to adopt SVMs for stressed speech classification.
最后一个主题将讲解支持向量机分类物中核心使用的可训练物件侦测系统。
The last topic is about a trainable object detection system using at its core a Support Vector Machine classifier.
还对支持向量机在多元分类中的应用进行了讨论，并给出了实例。
We also discussed the application of support vector machine in multivariate classification with some examples.
目前，支持向量机在模式识别、函数逼近、数据挖掘和文本自动分类中均有很好的应用。
Recently, Support Vector Machine is well applied in pattern recognition, function approximate, data mining and text auto categorization.
利用粗糙集理论，通过对训练集的学习，构造分类规则，对支持向量机反馈后的结果再次进行处理。
By using rough set theory, this paper structures classification rules and processes the support vector machine feedback results with learning the train set.
论文研究模糊支持向量分类机在冠心病诊断中的应用。
In this paper, we have studied on applying fuzzy support vector classification to coronary heart diagnose.
方法采用聚类分析原理和方法，将专家个体排序向量进行分类，根据分类结果确定专家权重系数。
Methods The collating vectors of individual expert are classified with hierarchical clustering principle and the expert weight coefficients are determined according to the result of classification.
针对电梯群控调度中的交通流模式识别问题，提出了一种基于多值分类支持向量机的电梯交通流模式识别方法。
Aiming at the pattern recognition of traffic flows in elevator group control systems, a method based on the multi-value classification SVM (Support Vector Machine) is put forward.
文档向量化的质量对于文本分类的速度和准确度有着很大的影响。
The vectorization of documents affects the speed and accuracy of text categorization greatly.
这一方法大大提高了支持向量机分类的泛化能力，从而大大提高了支持向量机的应用范围。
This approach greatly improves the generalization ability of SVM classification and its application area is extended.
提出了一种利用支持向量机(SVM)对机械系统故障进行分类的新方法；以二值分类为基础，开发了基于支持向量机的多值分类器。
A new method of fault classification for mechanical system by means of support vector machine (SVM) is proposed and a multi-class SVM classifier based on binary classification was developed.
首先利用色度矩提取葡萄病害叶片纹理图像的特征向量，然后将支持向量机分类方法应用于病害的识别。
At first, extracting features of chromaticity moments of texture image of grape disease is done, then classification method of SVM for recognition of grape disease is discussed.
首先利用色度矩提取植物病害叶片的特征向量，然后利用支持向量机分类方法进行病害的识别。
At first, extracting features of chromaticity moments was done, then classification method of SVM for recognition of plant disease was discussed.
然后基于支持向量机进行分类建模和预测。
Then, we realize classification modeling and forecasting test based on SVM.
然后基于支持向量机进行分类建模和预测。
Then, we realize classification modeling and forecasting test based on SVM.
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