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提出一种可有效处理多个数据集合之间变量关系的集成多核典型相关分析方法,构造一个特殊的核函数,使其更好地将原始样本数据映射到高维空间;基于支持向量机,在选择一个优化参数的基础上最大化多组数据集变量间的关系,以寻求整体相关性最大。在多特征手写体数字库上的实验证明,相比传统的典型相关分析与核典型相关分析方法,基于优化参数的集成多核典型相关分析方法具有更优的性能。 相似文献
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积雪面积是融雪径流模型中变量数据输入之一,准确的获取雪盖范围是进行流域尺度融雪水文过程研究的关键,在水资源管理及洪水预报中具有重要意义.本文以天山山区中段为例,利用MODIS数据,提出了结合混合光谱分解的积雪分量及灰度共生矩阵提取的纹理特征的SVM分类方法,对研究区积雪面积信息提取进行了研究.结果表明:通过利用混合光谱分解的积雪分量作为SVM的特征输入,总体分类精度比传统SVM分类结果有了一些提高.同时考虑结合基于灰度共生矩阵提取的纹理特征用于分类中,总体精度比传统SVM方法提高了1.081％,制图精度达到了99.01％.本文提出的分类方法能够适应特征组合之间的非线性关系,从而能提供更多的区域地物空间分布信息,能够调整无样本地表类型地区的积雪面积反演,对今后的融雪水文过程研究有重要意义. 相似文献
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通过分析数码相机的成像特点,文章提出了一种基于图像噪声的相机源辨识方法。通过抽取反映传感器噪声的特征,并训练分类器实现相机型号的辨识。实验结果表明所提取的新特征比前面算法所提取的特征能更好的体现同一品牌相机的模式是噪声的特点,因此具有更高的识别率,且鲁棒性更好。 相似文献
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《Information processing & management》2023,60(4):103378
To improve the effect of multimodal negative sentiment recognition of online public opinion on public health emergencies, we constructed a novel multimodal fine-grained negative sentiment recognition model based on graph convolutional networks (GCN) and ensemble learning. This model comprises BERT and ViT-based multimodal feature representation, GCN-based feature fusion, multiple classifiers, and ensemble learning-based decision fusion. Firstly, the image-text data about COVID-19 is collected from Sina Weibo, and the text and image features are extracted through BERT and ViT, respectively. Secondly, the image-text fused features are generated through GCN in the constructed microblog graph. Finally, AdaBoost is trained to decide the final sentiments recognized by the best classifiers in image, text, and image-text fused features. The results show that the F1-score of this model is 84.13% in sentiment polarity recognition and 82.06% in fine-grained negative sentiment recognition, improved by 4.13% and 7.55% compared to the optimal recognition effect of image-text feature fusion, respectively. 相似文献
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纵断面设计的任务就是根据汽车的动力特性,道路等级、当地的自然地理条件及工程经济性等研究起伏空间线等几何构成的大小及长度,以便达到行车安全迅速、运输经济合理及舒适的目的。根据某算例,对某公路进行纵断面设计。 相似文献
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《Journal of The Franklin Institute》2023,360(9):6585-6608
Accurate position and attitude information is an important basis for normal driving of intelligent vehicles. In this paper, we investigate the estimation of position and attitude states for intelligent vehicles with low cost scheme. The low cost GNSS, camera, and proprioceptive sensors equipped by mass-produced vehicle are fused to estimate the states. The visual measurements adopted in this paper are based on the lateral distance and deflection angle to road features such as lane lines or curbs, which are generated more frequently than some other semantic features such as traffic lights, and leads to broader application scenario. Moreover, it is easier to implement compared with geometrical feature matching methods, since it only needs a simple prior map while latter needs large maps containing many high precision features. The visual measurements is often with large time delay due to negligible processing time. In order to fuse delayed measurements, a state-augmentation technique is adopted for the estimator design. The performance of the proposed method is evaluated by professional simulation software CarMaker, and shows that the incorporation of road features based visual measurement can effectively improve the position and attitude estimation accuracy by reducing the lateral position and yaw angle estimation error. 相似文献
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We investigate how, and to what extent, morphological complexity of the language influences text classification using support vector machines (SVM). The Croatian–English parallel corpus provides the basis for direct comparison of two languages of radically different morphological complexity. We quantified, compared, and statistically tested the effects of morphological normalisation on SVM classifier performance based on a series of parallel experiments on both languages, carried over a large scale of different feature subset sizes obtained by different feature selection methods, and applying different levels of morphological normalisation. We also quantified the trade-off between feature space size and performance for different levels of morphological normalisation, and compared the results for both languages. Our experiments have shown that the improvements in SVM classifier performance is statistically significant; they are greater for small and medium number of features, especially for Croatian, whereas for large number of features the improvements are rather small and may be negligible in practice for both languages. 相似文献
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《Information processing & management》2023,60(1):103120
Multimodal fake news detection methods based on semantic information have achieved great success. However, these methods only exploit the deep features of multimodal information, which leads to a large loss of valid information at the shallow level. To address this problem, we propose a progressive fusion network (MPFN) for multimodal disinformation detection, which captures the representational information of each modality at different levels and achieves fusion between modalities at the same level and at different levels by means of a mixer to establish a strong connection between the modalities. Specifically, we use a transformer structure, which is effective in computer vision tasks, as a visual feature extractor to gradually sample features at different levels and combine features obtained from a text feature extractor and image frequency domain information at different levels for fine-grained modeling. In addition, we design a feature fusion approach to better establish connections between modalities, which can further improve the performance and thus surpass other network structures in the literature. We conducted extensive experiments on two real datasets, Weibo and Twitter, where our method achieved 83.3% accuracy on the Twitter dataset, which has increased by at least 4.3% compared to other state-of-the-art methods. This demonstrates the effectiveness of MPFN for identifying fake news, and the method reaches a relatively advanced level by combining different levels of information from each modality and a powerful modality fusion method. 相似文献
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Extracting semantic relationships between entities from text documents is challenging in information extraction and important for deep information processing and management. This paper proposes to use the convolution kernel over parse trees together with support vector machines to model syntactic structured information for relation extraction. Compared with linear kernels, tree kernels can effectively explore implicitly huge syntactic structured features embedded in a parse tree. Our study reveals that the syntactic structured features embedded in a parse tree are very effective in relation extraction and can be well captured by the convolution tree kernel. Evaluation on the ACE benchmark corpora shows that using the convolution tree kernel only can achieve comparable performance with previous best-reported feature-based methods. It also shows that our method significantly outperforms previous two dependency tree kernels for relation extraction. Moreover, this paper proposes a composite kernel for relation extraction by combining the convolution tree kernel with a simple linear kernel. Our study reveals that the composite kernel can effectively capture both flat and structured features without extensive feature engineering, and easily scale to include more features. Evaluation on the ACE benchmark corpora shows that the composite kernel outperforms previous best-reported methods in relation extraction. 相似文献
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在不同化工厂检测环境中,空气中的环境变化情况较为复杂,带有颜色特征的污染气体浓度会被迅速稀释,造成转化的像素特征强度衰减。传统算法多是基于采集到的某种像素特征强度进行污染等级的判断,一旦气体被稀释,颜色特征发生退化,检测准确率会降低。提出了一种基于像素支持向量机增量学习算法。通过灰度差分的支持向量机增量学习。建立对不同像素等级信号进行对应增强学习,克服像素衰退的弊端。实验证明,这种算法能够避免由于化工厂内气体大量扩散,造成的像素衰减的缺陷,提高了化工污染程度检测的准确率。 相似文献
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在MATLAB环境下,取ORL人脸数据库的部分人脸样本集,基于PCA方法提取人脸特征,形成特征脸空间,然后将每个人脸样本投影到该空间得到一投影系数向量,该投影系数向量在一个低维空间表述了一个人脸样本,这样就得到了训练样本集。同时将另一部分ORL人脸数据库的人脸作同样处理得到测试样本集。然后基于最近邻算法进行分类,得到识别率,接下来使用BP神经网络算法进行人脸识别,最后通过基于神经网络算法和最近邻算法进行综合决策,对待识别的人脸进行分类。 相似文献
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人体生物节律理论揭示了人的体力、情绪和智力的周期性变化规律,及其对人们行为的影响。驾驶员的行为是引发道路交通事故的主要因素。利用人体生物节律理论对驾驶员及交通运输企业进行安全行车的管理和指导,能够有效地减少和预防道路交通事故的发生,改善道路交通安全状况。 相似文献
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Nzeqqe Chantale Ebude Wang Mingyan 《科技广场》2007,(7):6-8
Most traffic control systems available in major cities in Cameroon are still out dated, make use of theories and models which are very slow to implement, waste resources and their efficiency is very low. Thus the number of road accidents encountered on our major highways and inter urban traffic networks has been increasing despite the various efforts made by government and certain NGO to synthesize road users on certain aspects of traffic control and safety road use. Taxis are not left out in the whole show and most of the blame always falls on them. The need to use available Information and Communication Technology ICT to improve on the control of traffic in inter urban cities and major highways is imperative. ITS optimizes the use of resources, reduce pollution, contribute to environmental protection and increases the national economy. . This paper starts by giving a brief situation of the transportation system in Cameroon, its drawbacks and proposes an optimized ITS based system. 相似文献
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关于支持向量回归机的模型选择 总被引：28，自引：0，他引：28
支持向量机是在统计学习理论基础上发展起来的一种新型的机器学习方法。模型选择是设计支持向量机的重要内容之一。本文在分析用于回归的支持向量机原理的基础上,分别从核函数的选择、模型参数的作用、模型参数的调整方法等模型选择方面进行了综述,并讨论了模型选择的优缺点,最后指出在实际应用中常见的核函数和模型参数调整方法。 相似文献
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互联网的发展逐渐改变了人们的生活方式,电子邮件因其方便、快捷的特点已受到人们的青睐。但许多垃圾邮件同时也在网络中蔓延,占据了邮件服务器的大量存储空间,用户往往需要花费大量的时间去删除这些垃圾邮件。因此,研究邮件的自动过滤具有重要意义。邮件的自动过滤主要有基于规则和基于统计两种方式。而目前基于统计的过滤器中,常用的贝叶斯方法等是建立在经验风险最小化的基础之上,过滤器推广性能较差。支持向量机(SVM)是在统计学习理论的基础上发展而来的一种新的模式识别方法,在解决有限样本、非线性及高维模式识别问题中表现出许多特有的优势。它不仅考虑了对推广能力的要求,而且追求在有限信息的条件下得到最优结果。因此,本文将支持向量机应用于邮件过滤,实验证明过滤效果较好。 相似文献
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《Information processing & management》2022,59(5):103003
Many methods of multi-kernel clustering have a bias to power base kernels by ignoring other kernels. To address this issue, in this paper, we propose a new method of multi-kernel graph fusion based on min–max optimization (namely MKGF-MM) for spectral clustering by making full use of all base kernels. Specifically, the proposed method investigates a novel min–max weight strategy to capture the complementary information among all base kernels. As a result, every base kernel contributes to the construction of the fusion graph from all base kernels so that the quality of the fusion graph is guaranteed. In addition, we design an iterative optimization method to solve the proposed objective function. Furthermore, we theoretically prove that our optimization method achieves convergence. Experimental results on real medical datasets and scientific datasets demonstrate that the proposed method outperforms all comparison methods and the proposed optimization method achieves fast convergence. 相似文献