To solve the problem, an improved SVM algorithm, DFP-PSVM, is presented in this paper.
为解决此问题，本文提出了一种改进的支持向量机算法——DF P - PS VM算法。
This paper proposes a weighted C-SVM algorithm and analyzes its classification performance theoretically.
提出了一种加权C—SVM分类算法，并从理论上分析了算法的性能。
Time series modeling and identification techniques were analyzed and the ARMA time series model based on robust LS-SVM algorithm was proposed.
对时序数据建模与辨识技术进行了分析，提出了使用鲁棒LS-SVM算法建立ARMA时序预测模型。
We can use term frequency to have a weighted calculation and improve traditional text similarity calculation probability model in SVM algorithm.
通过利用基于词频的权值计算，同时改进传统文本相似度计算概率模型，改进SVM算法实现邮件过滤系统。
Experiments are made on UCI database, and the results indicate the MBI-SVM algorithm has many good characteristics, such as stability, feasibility and practicality.
在UCI数据库上进行了实验，结果证实该算法具有较高的稳定性、可行性和实用性。
A method for pre-extracting support vectors based on concentric hyperspheres division is presented in this paper, and HD-SVM algorithm based on this method is presented also.
提出了一个基于同心超球面分割的支持向量预抽取方法，并在此基础上给出了HD - SVM训练算法。
Through SVM algorithm, solving the building problem of input sample feature vector (weak information sample) in the process of extracting mineralizing information from RS data.
解决了应用SVM识别算法对遥感矿化信息提取过程中输入样本特征向量（微弱信息样本）的构造问题。
The main target of the thesis is: for the shortcoming of the slowly training speed about SVM, we want to find a new SVM accelerated training algorithm based on the existing SVM algorithms.
本文主要的研究工作是：针对支持向量机训练速度慢的问题，在现有支持向量机加速训练算法的基础上，寻找一种新的SVM加速训练算法。
In this model, firstly use the SVM algorithm to filter all the input data, then the considered legitimate data is classified with KMO, so the decision-making module can respond the final input data.
该模型首先利用SVM算法对全部的输人数据进行区分，然后将其认为的合法数据集用KMO算法分类，以该结果作为决策模块的输入并做出最终的响应。
The paper explores possible applications of Prior knowledge Support Vector Machine (P-SVM) based data mining algorithm in an automatic TCM syndrome classification system.
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。
A new SVM iterative algorithm is proposed, aiming at the problem that the speeds of learning and classifying are slow in large training set.
针对SVM方法在大样本情况下学习和分类速度慢的问题，提出了大样本情况下的一种新的SVM迭代训练算法。
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.
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。
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.
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。
In order to research temperature trend forecasting of rolling electric machine, a temperature trend forecasting algorithm based on SVM is proposed.
为了对轧线电机温度趋势预测进行研究，提出了一种基于支持向量机的温度趋势预测算法。
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.
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。
The support vector machine (SVM) is an algorithm based on structure risk minimizing principle and high generalization ability.
支持向量机(SVM)是一种基于结构风险最小化原理，具有很好推广性能的学习算法。
The result proves that predicting network time-delay by SVM has greater accuracy than by linear prediction algorithm.
结果证明，和线性预测算法相比，采用支持向量机预测网络延时具有较高的正确率。
Subsequently those preprocessing data are the input of SVM (support vector machine) algorithm, which is used for ball bearing fault detection.
将预处理后数据作为SVM(支持向量机)算法的输入，通过SVM算法来检测轴承故障。
This paper studies a plane image recognition algorithm based on Support Vector Machine (SVM).
研究了一种基于支持向量机的飞机图像识别算法。
A liveness check algorithm based on body temperature measurement model using SVM (support vector machine) is proposed.
提出了一种基于支持向量机体温检测模型的活性判别算法。
In this paper we present a novel hybrid algorithm based on attribute reduction of RS and classification principles of SVM.
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。
Based on the equivalence between the original training set and the newly added training set, a new algorithm for SVM-based incremental learning was proposed.
基于原训练样本集和新增训练样本集在增量训练中地位等同，提出了一种新的SVM增量学习算法。
The experimental results show that SVM-based reputation evaluation algorithm can adapt to the grid environment and describe the reputation of grid members well.
仿真实验结果表明基于SVM的信誉评价算法能够较好地适应网格环境并能较客观及准确地评价网格成员的信誉。
This paper proposes an algorithm of shot boundary detection, which employs Support Vector Machine (SVM) to classify visual attention features based on the research results of psychology.
借鉴心理学中有关视觉注意的研究成果，提出了一种采用符合人类视觉注意的特征，并利用支持矢量机进行视频镜头边界检测的算法。
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。
The algorithm theory and realization process of the proposed tracking method based on SVM are introduced in detail.
论述了将SVM应用到目标跟踪领域的算法原理及实现过程。
With limited samples, SVM has stronger ability of generalization in comparison with existing machine learning algorithm.
与现有的机器学习算法相比，在样本有限的情况下，支撑矢量机具有更强的分类推广能力。
In addition, we combined SVM with rough set theory, and got rough support vector machine algorithm (RSVM).
此外，将支持向量机与粗糙集理论相结合，得到粗糙支持向量机算法(RSVM)。
In addition, we combined SVM with rough set theory, and got rough support vector machine algorithm (RSVM).
此外，将支持向量机与粗糙集理论相结合，得到粗糙支持向量机算法(RSVM)。
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