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网络教育已成为现代教育技术主流的发展方向。提出了二层树状结构模型,并在此基础上,设计了一个基于基于浏览行为的个性化推荐系统(BB IRS)。系统可以通过离线和在线方式对用户的访问日志和交互数据分别进行挖掘,并通过根据用户的浏览速度计算用户对页面的兴趣度,根据该兴趣度是否大于阈值,系统采用不同的推荐策略。 相似文献
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基于隐马尔科夫模型的个性化推荐系统,通过对模型进行三个层级的推荐设计,采用逐层递进的方式来实现个性化推荐目标。并且每个推荐层级针对不同的用户群体。对目标用户的历史观看记录中存在的兴趣关键词加权处理,以及建立用户兴趣时间转移阈值,推荐给资深用户最可能感兴趣的在线视频内容的方法,完成资深用户的个性化推荐目标。 相似文献
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一种基于稀疏矩阵划分的个性化推荐算法 总被引:13,自引:0,他引:13
文章提出稀疏矩阵划分的思想,对资源评分矩阵进行划分,缩小近邻搜索的范围和需要预测的资源数目,减少数据稀疏性,提高了个性化推荐算法的可扩展性。另外,分别讨论了采取分类和聚类的方法对稀疏矩阵进行划分。实验结果表明:基于稀疏矩阵划分的个性化推荐算法在算法性能上优于传统协同过滤算法。 相似文献
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基于Web挖掘的个性化学习系统 总被引:1,自引:0,他引:1
随着Internet的深入发展和不断的普及,Web已经成为人们获取信息,进行学习的最重要的手段之一.但是,目前Web系统只为所有用户提供相同的服务,而Web用户的需求却千差万别,用户希望Web系统能够根据他们的不同特性提供个性化的服务.普通的学习系统已经不能适应他们的学习,不能体现他们的个性化.因此,根据他们的不同特性开发个性化的学习系统已变得相当重要. 相似文献
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用户兴趣模型是推荐系统产生个性化推荐的主要知识源,是实现个性化推荐的关键.针对用户兴趣进行建模是个性化推荐系统实现过程中的一个重要环节,本文从高校就业网站用户对象的特点出发,提出了一种将用户显性兴趣与隐性兴趣相结合的动态建模方法,此方法能有效的为用户对象进行推荐。 相似文献
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针对Web挖掘中异构数据源和半结构化的特点,提出基于XML个性化Web挖掘框架模型实现用户兴趣数据的挖掘。分析模型的工作流程,重点讨论实现该模型的关键技术:XML文档统一模型、Web内容预处理模块和Web内容挖掘模块。 相似文献
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针对高等学校学生选课系统中存在的缺乏个性化课程推荐、选课效率较低的问题,通过对个性化推荐技术的分析研究,提出了基于内容、项目及用户属性的改进混合模式算法,并将该算法应用到选课系统中,用MACE数据集对算法进行验证。结果表明,该算法解决了个性化推荐技术中的冷启动问题,相关指标有明显提高,实现了课程与新课程的个性化推荐,并减少了选课的盲目性。 相似文献
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Theodore S. Stamoulakatos Efstathios D. Sykas 《Wireless Communications and Mobile Computing》2007,7(4):415-429
In this study, we present a technique that combines pattern recognition techniques with cellular signaling measurements and more precisely information extracted from Abis air interface in GSM network. The pattern recognition is applied to measurement reports that mobile terminal (MT) sends to its serving base station (BS). Modeling of these reports is performed by hidden Markov model (HMM) while employing clustering large applications (CLARA) as clustering method. The accurate results during MT velocity estimation located inside a probe vehicle show the potential of the method when applied to large scale of MTs in order to estimate basic parameters for road traffic. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
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This paper presents a novel approach for human activity recognition (HAR) using the joint angles from a 3D model of a human body. Unlike conventional approaches in which the joint angles are computed from inverse kinematic analysis of the optical marker positions captured with multiple cameras, our approach utilizes the body joint angles estimated directly from time‐series activity images acquired with a single stereo camera by co‐registering a 3D body model to the stereo information. The estimated joint‐angle features are then mapped into codewords to generate discrete symbols for a hidden Markov model (HMM) of each activity. With these symbols, each activity is trained through the HMM, and later, all the trained HMMs are used for activity recognition. The performance of our joint‐angle–based HAR has been compared to that of a conventional binary and depth silhouette‐based HAR, producing significantly better results in the recognition rate, especially for the activities that are not discernible with the conventional approaches. 相似文献
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一种基于隐马尔可夫模型的IDS异常检测新方法 总被引:3,自引:1,他引:3
提出一种新的基于隐马尔可夫模型的异常检测方法,主要用于以shell命令或系统调用为原始数据的IDS。此方法对用户(或程序)行为建立特殊的隐马尔可夫模型,根据行为模式所对应的序列长度对其进行分类,将行为模式类型同隐马尔可夫模型的状态联系在一起,并引入一个附加状态。由于模型中各状态对应的观测值集合互不相交,模型训练中采用了运算量较小的的序列匹配方法,与传统的Baum-Welch算法相比,大大减小了训练时间。根据模型中状态的实际含义,采用了基于状态序列出现概率的判决准则。利用UNIX平台上用户shell命令数据进行的实验表明,此方法具有很高的检测准确性,其可操作性也优于同类方法。 相似文献
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《AEUE-International Journal of Electronics and Communications》2014,68(3):227-236
Human recognition is an essential requirement for human-centric surveillance, activity recognition, gait recognition etc. Inaccurate recognition of humans in such applications may leads to false alarm and unnecessary computation. In the proposed work a robust background modeling algorithm using fuzzy logic is used to detect foreground objects. Three distinct features are extracted from the contours of detected objects. An unique aggregated feature vector is formed using a fuzzy inference system by aggregating three feature vectors. To minimize computation in recognition using Hidden Markov model (HMM), the length of final feature vector is reduced using vector quantization. The proposed method is explained using five basic phases; background modeling and foreground object detection, features extraction, aggregated feature vector calculation, vector quantization, and recognition using Hidden Markov model. 相似文献
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Comparison of Khasi Speech Representations with Different Spectral Features and Hidden Markov States 下载免费PDF全文
Bronson Syiem Sushanta Kabir Dutta Juwesh Binong Lairenlakpam Joyprakash Singh 《电子科技学刊:英文版》2021,19(2):155-162
In this paper, we present a comparison of Khasi speech representations with four different spectral features and novel extension towards the development of Khasi speech corpora. These four features include linear predictive coding (LPC), linear prediction cepstrum coefficient (LPCC), perceptual linear prediction (PLP), and Mel frequency cepstral coefficient (MFCC). The 10-hour speech data were used for training and 3-hour data for testing. For each spectral feature, different hidden Markov model (HMM) based recognizers with variations in HMM states and different Gaussian mixture models (GMMs) were built. The performance was evaluated by using the word error rate (WER). The experimental results show that MFCC provides a better representation for Khasi speech compared with the other three spectral features. 相似文献
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本文利用隐马尔可夫随机场和高斯模型分别建立标号场和特征场的邻域关系,提出了基于隐马尔可夫高斯随机场模型的模糊聚类分割算法。该算法用隐马尔可夫随机场模型定义先验概率,并将该先验概率作为尺度控制因子引入到KL(Kullback-Lerbler)信息中,在目标函数的定义中,KL信息作为规则化项,其系数表示算法的模糊程度。在基于高斯模型的后验概率中,像素相关性被定义在空间和谱间,并用该概率的负对数值表征像素点到聚类中心的非相似性测度。通过对合成遥感影像和高分辨率遥感影像进行分割实验,证明了算法的有效性和普适性。 相似文献
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