微博转发预测算法评测系统的建立及性能比较

为解决微博转发行为预测问题,提出了一个基本的预测算法评测系统框架.该系统解决了原始微博数据的抓取和预处理,以及用户微博的转发和忽略行为的判定,微博和用户的特征提取等问题,为解决微博转发行为预测问题提供了技术基础.详细分析了现有文献和工作中的微博转发行为预测算法,阐述了它们的基本原理.通过定量实验分析了不同微博转发行为预测算法的在局部预测问题和全局预测问题方面的性能,并且从算法的原理等方面出发给出了定性的分析.     

基于LLE算法和SVM的旋转机械故障诊断

利用LLE（Locally Linear Embedding）算法对众多的观测变量进行降维,再利用支持向量分类器SVM（Support Vector Machine）方法对降维后的变量数据集进行故障诊断。通过算例仿真表明,旋转机械故障的23维变量因素可降到14维,同时得到的诊断结果中,训练集的正确率为94．8％,测试集的正确率为100％。结果表明基于LLE算法和SVM的旋转机械故障诊断的模型精度有效。其既降低了模型的复杂度,又不影响故障诊断模型的精度。     

考场异常行为检测算法

以智能视频监控理论为依据,结合考试现场特点,提出了一种能够进行考场异常行为检测的高效算法。该算法从考生信息结构和内容方面作了科学设计,提出了行为覆盖区、3维考场关注度等概念。仿真实验针对分析器的准确率和效率两方面进行,并特别比较了本算法设计的分析器和普通方式分析器的效率。实验结果表明,本算法能很好地挖掘视频帧间的历史关系,与未采用本算法的普通方式相比检测效率有较大提高。     

足球视频镜头分类方法

根据视频语义分析和视频摘要等应用对于视频数据结构化的需求,提出了一种针对足球视频的镜头分类方法.通过logo模板匹配检测并定位出视频中的慢镜头,对其余的正常比赛部分做镜头边界检测完成视频切分.基于分块的思想,对正常比赛镜头帧计算其各块的场地像素比率值作为特征,利用SVM分类器将正常比赛镜头分为远镜头、中镜头、球员特写或场外镜头3类.至此,整个视频流可以表示为结构化的四类镜头类型标示序列.实验结果表明,该方法在视频切分和镜头类型识别的准确性方面具有良好的效果.     

Online motion recognition using an accelerometer in a mobile device

This paper introduces a new method to implement a motion recognition process using a mobile phone fitted with an accelerometer. The data collected from the accelerometer are interpreted by means of a statistical study and machine learning algorithms in order to obtain a classification function. Then, that function is implemented in a mobile phone and online experiments are carried out. Experimental results show that this approach can be used to effectively recognize different human activities with a high-level accuracy.     

Identification of EMG signals using discriminant analysis and SVM classifier

The electromyography (EMG) signal is a bioelectrical signal variation, generated in muscles during voluntary or involuntary muscle activities. The muscle activities such as contraction or relaxation are always controlled by the nervous system. The EMG signal is a complicated biomedical signal due to anatomical/physiological properties of the muscles and its noisy environment. In this paper, a classification technique is proposed to classify signals required for a prosperous arm prosthesis control by using surface EMG signals. This work uses recorded EMG signals generated by biceps and triceps muscles for four different movements. Each signal has one single pattern and it is essential to separate and classify these patterns properly. Discriminant analysis and support vector machine (SVM) classifier have been used to classify four different arm movement signals. Prior to classification, proper feature vectors are derived from the signal. The feature vectors are generated by using mean absolute value (MAV). These feature vectors are provided as inputs to the identification/classification system. Discriminant analysis using five different approaches, classification accuracy rates achieved from very good (98%) to poor (96%) by using 10-fold cross validation. SVM classifier gives a very good average accuracy rate (99%) for four movements with the classification error rate 1%. Correct classification rates of the applied techniques are very high which can be used to classify EMG signals for prosperous arm prosthesis control studies.     

A tunable support vector machine assembly classifier for epileptic seizure detection

Automating the detection of epileptic seizures could reduce the significant human resources necessary for the care of patients suffering from intractable epilepsy and offer improved solutions for closed-loop therapeutic devices such as implantable electrical stimulation systems. While numerous detection algorithms have been published, an effective detector in the clinical setting remains elusive. There are significant challenges facing seizure detection algorithms. The epilepsy EEG morphology can vary widely among the patient population. EEG recordings from the same patient can change over time. EEG recordings can be contaminated with artifacts that often resemble epileptic seizure activity. In order for an epileptic seizure detector to be successful, it must be able to adapt to these different challenges. In this study, a novel detector is proposed based on a support vector machine assembly classifier (SVMA). The SVMA consists of a group of SVMs each trained with a different set of weights between the seizure and non-seizure data and the user can selectively control the output of the SVMA classifier. The algorithm can improve the detection performance compared to traditional methods by providing an effective tuning strategy for specific patients. The proposed algorithm also demonstrates a clear advantage over threshold tuning. When compared with the detection performances reported by other studies using the publicly available epilepsy dataset hosted by the University of BONN, the proposed SVMA detector achieved the best total accuracy of 98.72%. These results demonstrate the efficacy of the proposed SVMA detector and its potential in the clinical setting.     

Leukocyte image segmentation using simulated visual attention

Computer-aided automatic analysis of microscopic leukocyte is a powerful diagnostic tool in biomedical fields which could reduce the effects of human error, improve the diagnosis accuracy, save manpower and time. However, it is a challenging to segment entire leukocyte populations due to the changing features extracted in the leukocyte image, and this task remains an unsolved issue in blood cell image segmentation. This paper presents an efficient strategy to construct a segmentation model for any leukocyte image using simulated visual attention via learning by on-line sampling. In the sampling stage, two types of visual attention, “bottom-up” and “top-down” together with the movement of the human eye are simulated. We focus on a few regions of interesting and sample high gradient pixels to group training sets. While in the learning stage, the SVM (support vector machine) model is trained in real-time to simulate the visual neuronal system and then classifies pixels and extracts leukocytes from the image. Experimental results show that the proposed method has better performance compared to the marker controlled watershed algorithms with manual intervention and thresholding-based methods.     

Detection of epileptic electroencephalogram based on Permutation Entropy and Support Vector Machines

The electroencephalogram (EEG) has proven a valuable tool in the study and detection of epilepsy. This paper investigates for the first time the use of Permutation Entropy (PE) as a feature for automated epileptic seizure detection. A Support Vector Machine (SVM) is used to classify segments of normal and epileptic EEG based on PE values. The proposed system utilizes the fact that the EEG during epileptic seizures is characterized by lower PE than normal EEG. It is shown that average sensitivity of 94.38% and average specificity of 93.23% is obtained by using PE as a feature to characterize epileptic and seizure-free EEG, while 100% sensitivity and specificity were also obtained in single-trial classifications.     

基于SVM和PSO的图像质量评价

为提高白噪声、高斯模糊、JPEG2000压缩等失真类型图像的评价准确率,提出一种基于支持向量机和粒子群优化算法的图像质量评价方法。提取样本图像数据和确定评价指标,对样本数据进行预处理。利用粒子群优化算法选择最优参数,使用最优参数对训练集数据进行训练,对预测集数据进行预测分析,并建立图像质量评价模型。实验结果表明,与线性回归模型、BP神经网络模型等传统方法相比,该方法的评价准确率较高,能够准确地反映人眼对图像的视觉感知。