对于已知行为，采用加权支持向量机分类算法来识别其行为类别；
For the known activities, the weighted support vector machine (WSVM) is used to recognize their types.
针对基于基因表达数据的分类，本文从特征基因选择和支持向量机分类算法两个方面进行了改进。
This thesis improves classification using gene expression data method in two aspects: feature selection and SVMs classification algorithm.
其中，利用支持向量机分类算法得到的分类模型对呆鲦鱼和蜜蜂毒性测试集的整体预测准确度分别达到95.9%和95.0%。
The overall predictive accuracy of the classification models using support vector machine were 95.9% for the fathead minnow test set and 95.0% for the honey bee test set.
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.
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.
介绍了支持向量机分类和回归算法，将其应用于梁结构的损伤诊断中。
This paper introduces the support vector classification and regression algorithms, which are applied to the structure damage identification.
通过支持向量机训练算法对基因表达数据进行分类训练，为分析基因数据提供有效的手段。
Through support vector machine algorithms for gene expression data classification training, SVMs provide a effective way for analysis of gene expression data.
为了将一般增量学习算法扩展到并行计算环境中，提出一种基于多支持向量机分类器的增量学习算法。
In order to extend common incremental learning algorithms into a parallel computation setting, an incremental learning algorithm with multiple support vector machine classifiers is proposed.
基于小波分解提取人脸特征技术和多分类支持向量机模型，提出了一种新的准正面人脸识别算法。
This paper presents a novel algorithm for quasi-frontal face recognition based on the wavelet decomposition technique and a multi-class Support Vector Machine (SVM) model.
为了解决支持向量机的分类仅应用于较小样本集的问题，提出了一种密度聚类与支持向量机相结合的分类算法。
To solve the problem that support vector machine(SVM) can only classify the small samples set, a new algorithm which applied SVM to density clustering is proposed.
本文首先详细介绍了支持向量机的原理和算法，根据支持向量机的分类特性，提出将支持向量机应用到FSK解码技术中。
This paper introduces the theory and algorithm of SVM at first. It proposes to use the SVM to decode FSK signal based on the classifiable character of SVM.
针对结构隐式极限状态函数的可靠性分析，提出了一种支持向量机分类迭代算法。
For reliability analysis of structure with implicit limit state function, an iterative algorithm was presented on the basis of support vector classification machine.
结合粗糙集的属性约简和支持向量机的分类机理，提出了一种混合算法。
In this paper we present a novel hybrid algorithm based on attribute reduction of RS and classification principles of SVM.
给出带有模糊决策的模糊机会约束规划模型，在此基础上，研究模糊线性支持向量分类机(算法)和模糊线性支持向量回归机(算法)。
Proposed the model of fuzzy chance constrained programming with fuzzy decision, and did some research on fuzzy linear support vector regression (algorithm) on this base.
然后基于支持向量机算法构造了支持向量机分类器，将其用于心电图分类，取得了较高的准确率。
Then a support vector machine classifier is constructed and applied to ECG classification. Comparing with the classification of ECG by eyes, the classification results is much more precise.
现有的分类预测的方法有许多种，常见的有决策树算法(C4.5)、贝叶斯分类算法、BP算法与支持向量机等。
There are many classification methods to forecast such as decision tree algorithm (C4.5), Bayes algorithm, BP algorithm and SVM.
算法从位平面相关性的角度出发提取特征值，使用支持向量机作为分类器，对LSB匹配算法进行隐写分析。
From the perspective of bit plane correlation, this algorithm extracts features, USES support vector machine as a classifier to detect LSB matching.
采用了一种基于编码支持向量机的多分类方法，该方法解决了SVM多分类问题的同时，有效地减少了训练和测试时间，提高了算法的效率。
We solve the multi-classification task by using a so called Coding SVM. By using this algorithm, we not only solve the classification task but also reduce the training and testing time.
提出了一种基于RASTA滤波技术的多维语音特征和支持向量机分类的VAD算法，适用于低信噪比情况下的话音检测。
VAD algorithm based on RASTA-filter multi-dimensional speech feature and Support Vector Machine is presented. It applies to the speech detection under the low SNR conditions.
实验结果表明，基于KPCA特征提取法的支持向量机分类器的分类错误率在这四种分类算法中最低。
The experiment results concludes that the SVM classification method based on KPCA have the better classification effect than the other three.
本算法在保证分类准确度的同时，在增量学习问题上比传统的支持向量机有效。
This algorithm, in the incremental study question, is more effective than the traditional support vector machine, with assuring the classify accuracy.
采用不同核函数的支持向量机算法验证分类能力。
The classification ability was verified by support vector machines algorithm with various kernel functions.
分类部分，论文在理论上分析了文本分类采用支持向量机技术的优点，对两种具体的SVM算法-C-SVC和V-SVC进行了研究并利用实例进行验证。
The two classical SVM algorithms-C-SVC algorithm and S-SVC algorithm have been done more research and the two algorithms performance has been compared by using practice data.
集的属性约简和支持向量机的分类机理，提出了一种混合算法。
This article advanced a admixture arithmetic based on rough sets theory and via support vector machines.
该文提出的两种算法其核心均是利用支持向量机方法对样本进行分类。
The cores of the two algorithms presented in this paper are to classify the samples with SVM method.
该文提出的两种算法其核心均是利用支持向量机方法对样本进行分类。
The cores of the two algorithms presented in this paper are to classify the samples with SVM method.
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