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1.
李晨  王巍 《电子设计工程》2012,20(12):180-183
文中在研究现有先验知识与支持向量机融合的基础上,针对置信度函数凭经验给出的不足,提出了一种确定置信度函数方法,更好地进行分类。该方法是建立在模糊系统理论的基础上:将样本的紧密度信息作为先验知识应用于支持向量机的构造中,在确定样本的置信度时,不仅考虑了样本到所在类中心之间的距离,还考虑样本与类中其它样本之间的关系,通过模糊连接度将支持向量与含噪声样本进行区分。文中将基于先验知识的支持向量机应用于医学图像分割,以加拿大麦吉尔大学的brainWeb模拟脑部数据库提供的不同噪声的图像进行实验,实验结果表明采用基于先验知识的支持向量机比传统支持向量机具有更好的抗噪性能及分类能力。  相似文献   

2.
基于支持向量机的病毒程序检测方法   总被引:1,自引:0,他引:1  
彭宏  王军 《电子学报》2005,33(2):276-278
支持向量机是一种对于小样本具有良好学习性能的机器学习方法.本文将支持向量机方法用于病毒程序的检测中,可以改善其它方法在先验知识较少情况下的推广能力的问题.仿真实验结果看出,该方法在训练样本数相对较少的情况下,仍然具有较高的检测率和正确率,同时也具有较低的虚警率.  相似文献   

3.
基于主成分分析的支持向量机回归预测模型   总被引:2,自引:0,他引:2  
首先利用主成分分析法降低样本数据的维数,建立主成分的多元回归预测模型,其次利用支持向量机方法确定回归模型的系数,最后实例说明了该模型具有较高预测精度.  相似文献   

4.
支持向量机是基于统计学习理论的一种新兴的模式识别方法,在解决小样本、非线性及高维模式识别问题中表现出了突出的优势。但其支持向量的选取相当困难,这也成为限制其应用的瓶颈问题。本文提出了一种支持向量预选取的方法—K边界近邻法。该方法能有效提取包含支持向量的边界向量机,在不影响分类性能的情况下,极大减少了训练样本,提高训练速度。且新方法避免了数据分布的影响及对先验知识的依赖。仿真实验证明了该方法的可行性和有效性。  相似文献   

5.
基于小波分解和支持向量机的准正面人脸识别方法   总被引:5,自引:0,他引:5  
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。小波分解提取人脸特征具有对表情变化不敏感的特点;支持向量机作为分类器被认为具有很高的推广(generalization)性能,无需先验知识。在所提出的算法中,首先对训练图像进行预处理,然后使用小波分解方法对人脸图像进行特征提取,用所提取的人脸特征向量训练多分类支持向量机模型,最后用训练好的支持向量机进行人脸识别。利用ORL人脸图像库对该算法的实验测试结果,以及与其它人脸识别方法的比较结果表明了该算法在识别性能方面的优越性。  相似文献   

6.
马蕾 《电子科技》2013,26(9):10-13
将基于粒子群算法的支持向量机与半监督学习理论相结合,提出了粒子群算法支持向量机的半监督回归模型。针对典型的实验数据集进行实验,并将实验结果与常规的遗传算法支持向量机和粒子群支持向量机模型进行对比。实验结果表明,粒子群算法支持半监督回归模型明显提高了回归估计的精度。  相似文献   

7.
自适应迭代最小二乘支持向量机回归算法   总被引:4,自引:0,他引:4       下载免费PDF全文
 基于最小二乘支持向量机回归算法,本文在前期工作的基础上进行了扩展,提出了更加详尽的自适应迭代最小二乘支持向量机回归算法. 与标准的LSSVR相比,本文提出的算法在学习新样本的时候利用了已有的学习结果,可以快速获得新的学习机. 模拟结果表明,自适应迭代最小二乘支持向量机回归算法能够自适应地确定支持向量的数目,保留了QP方法在训练SVM时支持向量的稀疏性,在相近的回归精度下,该算法极大地提高了标准LSSVR学习的速度.  相似文献   

8.
《信息技术》2018,(4):37-40
文中提出了一种动态改进的遗传算法和支持向量回归机相耦合的水质预测方法,改进了传统遗传算法中交叉和变异概率固定的问题,尽可能避免陷入局部最优的问题。在对大理弥苴河水质进行大量实际监测的基础上,分别采用BP神经网络,遗传算法优化的支持向量回归机和自适应遗传算法优化的支持向量回归机3种模型的方法,建立了弥苴河水质高锰酸盐含量的的预测模型。通过数据预处理,筛选了60天的数据进行训练学习和测试。通过对三个模型的预测误差分析对比,可以得出自适应遗传算法优化支持向量回归的预测模型精度更高。  相似文献   

9.
利用支持向量机分类器中支持向量分布的几何意义,构造了一种新的与样本分布相关的推广能力预测模型,该模型充分利用了支持向量分布的先验信息,它与统计学习理论中推广能力准则具有一致的几何意义.首先利用支持向量分布的几何意义出发从海量样本中选择有效边界向量代替原有训练样本,然后在有效边界向量中分别计算最小包含半径和最大分类间隔.它不需要求解二次规划就可以得到与训练样本相关的推广能力计算模型,计算量较低.本文最后的最优核函数、核参数选择仿真实验结果表明本文提出的基于几何分析的支持向量机推广能力推测模型的合理性与高效性, 该模型对于解决支持向量机中最优核函数、核参数选择具有重要意义.  相似文献   

10.
开展煤层注水效果的精准化预测对于优化注水工艺过程、调整流程参数具有直接而显著的决定作用。作为非线性预测回归问题,煤层注水效果预测兼具小样本数据训练的特点,基于此,文章利用支持向量机小样本强泛化能力构建基于PSO优化支持向量机参数的煤层注水效果预测模型,通过对所获取样本数据的学习与训练过程,得到最优的支持向量机预测模型。实验结果显示:文章所提出的预测模型与传统的BP神经网络预测模型相比较具有明显的精度优势,可达到相对误差2.026%,对于指导煤层注水实际工程具有较强的应用价值。  相似文献   

11.
Timely and accurate information about incipient faults in online machines will greatly enhance the development of optimal maintenance procedures. The application of support vector regression to machine health monitoring was recently investigated; however, such implementation is based on batch processing of the available data. Therefore, the addition of new sample to the already existing dataset requires that the technique should retrain from scratch. This disadvantage makes the technique unsuitable for online systems that will give real-time information to field engineers so that corrective actions could be taken before there is any damage to the system. This paper presents an application of accurate online support vector regression (AOSVR) approach that efficiently updates a trained predictor whenever a new sample is added to the training set using shaft misalignment and nuclear power plant feedwater flow rate data. The results show that the approach is effective for online machine condition monitoring where it is usually difficult to obtain sufficient training data prior to the installation of the online systems.  相似文献   

12.
Realistic dynamics models are important for haptic display for virtual reality systems. Such dynamic models are desirably obtained via experimental identification. However, traditional dynamics identification methods normally require large sized training data sets, which maybe difficult to meet in many practical applications. To obtain the dynamics models, we present, in this paper, an identification method using support vector machines regression algorithm which is more effective than traditional methods for sparse training data. This method can be used as a generic learning machine or as a special learning technique that can make full use of the available knowledge about the dynamics structure. The experimental results show the application of our method for identifying friction models for haptic display.  相似文献   

13.
A general problem of supervised remotely sensed image classification assumes prior knowledge to be available for all the thematic classes that are present in the considered dataset. However, the ground-truth map representing that prior knowledge usually does not really describe all the land-cover typologies in the image, and the generation of a complete training set often represents a time-consuming, difficult and expensive task. This problem affects the performances of supervised classifiers, which erroneously assign each sample drawn from an unknown class to one of the known classes. In the present paper, a classification strategy is described that allows the identification of samples drawn from unknown classes through the application of a suitable Bayesian decision rule. The proposed approach is based on support vector machines (SVMs) for the estimation of probability density functions and on a recursive procedure to generate prior probability estimates for known and unknown classes. In the experiments, both a synthetic dataset and two real datasets were used.  相似文献   

14.
Training and testing different models in the field of text classification mainly depend on the pre-classified text document datasets. Recently, seven datasets have emerged for Arabic text classification, including Single-Label Arabic News Articles Dataset (SANAD), Khaleej, Arabiya, Akhbarona, KALIMAT, Waten2004, and Khaleej2004. This study investigates which of these datasets can provide significant training and fair evaluation for text classification. In this investigation, well-known and accurate learning models are used, including naive Bayes, random forest, K-nearest neighbor, support vector machines, and logistic regression models. We present relevance and time measures of training the models with these datasets to enable Arabic language researchers to select the appropriate dataset to use based on a solid basis of comparison. The performances of the five learning models across the seven datasets are measured and compared with the performance of the same models trained on a well-known English language dataset. The analysis of the relevance and time scores shows that training the support vector machine model on Khaleej and Arabiya obtained the most significant results in the shortest amount of time, with the accuracy of 82%.  相似文献   

15.
白宁 《现代电子技术》2013,(24):22-24,28
针对支持向量机(svM)模型不能有效处理海量数据挖掘的问题,提出一种改进的基于主动学习的支持向量机(AL_SVM)方法。该方法首先将训练集随机划分为多个独立同分布的子集,并选择其中一个子集作为初始训练集来训练SVM得到初始分类器和支持向量集,然后根据已经得到的分类器信息在剩余样本集中选择对于分类器改进作用最大的有价值样本。并与已得到的支持向量集合并构成新训练集,以更新分类器,从而在保留重要支持向量信息的前提下,去除大量不重要的支持向量,一定程度上避免了过学习问题,提高了学习效率。实验表明,AL_SVM方法能够在保持学习器泛化能力的同时提高其学习效率。  相似文献   

16.
针对支持向量机理论中存在的问题:训练样本数量多以及必须满足MerCer条件等,提出了一种基于相关向量机(RVM)的网络入侵检测方法。首先采用“删除特征”法对KDD99数据集中的41个特征进行评级,筛选出针对不同入侵类型的重要特征和非重要特征,然后只选择重要特征进行匹配。结果表明,这种方法与基于支持向量机(SVM)的入侵检测模型相比,具有更高的检测率和更低的误警率。  相似文献   

17.
In this paper, we present a fully automated computer-aided diagnosis (CAD) program to detect temporal changes in mammographic masses between two consecutive screening rounds. The goal of this work was to improve the characterization of mass lesions by adding information about the tumor behavior over time. Towards this goal we previously developed a regional registration technique that finds for each mass lesion on the current view a location on the prior view where the mass was most likely to develop. For the task of interval change analysis, we designed two kinds of temporal features: difference features and similarity features. Difference features indicate the (relative) change in feature values determined on prior and current views. These features may be especially useful for lesions that are visible on both views. Similarity features measure whether two regions are comparable in appearance and may be useful for lesions that are visible on the prior view as well as for newly developing lesions. We evaluated the classification performance with and without the use of temporal features on a dataset consisting of 465 temporal mammogram pairs, 238 benign, and 227 malignant. We used cross validation to partition the dataset into a training set and a test set. The training set was used to train a support vector machine classifier and the test set to evaluate the classifier. The average A(z) value (area under the receiver operating characteristic curve) for classifying each lesion was 0.74 without temporal features and 0.77 with the use of temporal features. The improvement obtained by adding temporal features was statistically significant (P = 0.005). In particular, similarity features contributed to this improvement. Furthermore, we found that the improvement was comparable for masses that were visible and for masses that were not visible on the prior view. These results show that the use of temporal features is an effective approach to improve the characterization of masses.  相似文献   

18.
郭楚栩  施勇  薛质 《通信技术》2020,(2):421-426
在入侵检测系统发展的30年间,不断有新的检测方法被提出。在如今的第四次工业革命——人工智能的潮流中,机器学习算法为各种系统的方法解决提供了新的思路。基于2018年Daniel Fraunholz等人提出了的入侵检测模型,提出了一种基于机器学习的端口扫描检测系统,其中系统的特征提取参考了KDD Cup 99数据集中数据的特征提取,而其中的模型训练集是基于CICIDS2017数据集的。最后,模型测试结果优良。  相似文献   

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