共查询到18条相似文献,搜索用时 109 毫秒
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为了能够正确地理解医疗概念和精确地分析临床记录,提出了一种基于概念信息量的方法来衡量概念之间的语义相似度.引进了计算概念信息量的算法,从医疗本体的分类知识中来计算概念的信息量.介绍和分析了常用的语义相似度算法,根据概念的信息量来重定义这些语义相似度算法,产生新的基于概念信息量的语义相似度算法.通过使用一个医疗术语的评估标准和一个标准的医疗本体来评估和比较这些算法.实验结果表明,相比常用的语义相似度算法,重定义后的算法有效地改善了概念相似性评估的准确性. 相似文献
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《计算机应用与软件》2016,(10)
在基于距离的语义相似度计算方法的基础上,综合多种因素对相似度的影响,提出一种新的相似度和相关度计算方法。将其应用到教学资源领域本体,计算本体概念间的相似度和相关度。实验结果显示该算法可以提高传统基于距离的相似度算法的性能。最后比较了利用该算法的语义查询与传统关键字查询的结果。 相似文献
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语义相似度的计算是自然语言处理中的重要研究内容,在过去几十年的研究工作中,已有大量的语义相似度计算方法被提出并广泛应用于语义消歧、文本聚类等领域中。基于WordNet本体,改进了信息量IC计算模型,进而提出了两种混合式的语义相似度的计算方法。实验结果表明,由于同时考虑了概念节点在WordNet中的最短路径距离和IC语义距离,所提方法优于已有方法,其计算结果更加接近人类的主观判断。 相似文献
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《计算机应用与软件》2018,(4)
为了实现本体概念的自动更新,减少对领域专家的过多依赖,给出一种基于语义相似度的本体概念更新方法 SSOCUM(Semantic Similarity-based Ontology Concept Update Method)。实现一种改进的基于Word Net的相似度算法,该算法在计算路径长度的基础上,综合考虑了概念的节点深度以及信息量对相似度的影响。为了弥补基于Word Net的相似度算法没有考虑概念属性所携带的语义信息的不足,加入属性相似度对其进行调整。通过实验对比,验证了改进算法的计算结果与标准数据集之间的皮尔森系数高于传统算法,计算结果更接近于人的主观判断。采用构建好的煤矿领域通风系统本体对SSOCUM算法进行实验分析。结果表明,SSOCUM算法有助于本体新概念的自动添加,并具有一定的准确性和有效性。 相似文献
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现有的语义Web服务匹配算法没有考虑到本体概念间的多元关系,导致概念的语义不能被完整地反映出来,从而影响了算法的匹配性能。利用本体概念间的多元关系定义了一种语义距离,并通过该语义距离给出了概念间的语义相似度计算方法,在此基础上提出基于语义相似度的Web服务匹配算法。该算法通过本体概念间的语义相似度来反映Web服务的匹配程度。最后,通过对比实验验证了该算法的可行性和有效性。 相似文献
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Anne H.H. Ngu Quan Z. Sheng Du Q. Huynh Ron Lei 《The VLDB Journal The International Journal on Very Large Data Bases》2001,9(4):279-293
The optimized distance-based access methods currently available for multidimensional indexing in multimedia databases have
been developed based on two major assumptions: a suitable distance function is known a priori and the dimensionality of the
image features is low. It is not trivial to define a distance function that best mimics human visual perception regarding
image similarity measurements. Reducing high-dimensional features in images using the popular principle component analysis
(PCA) might not always be possible due to the non-linear correlations that may be present in the feature vectors. We propose
in this paper a fast and robust hybrid method for non-linear dimensions reduction of composite image features for indexing
in large image database. This method incorporates both the PCA and non-linear neural network techniques to reduce the dimensions
of feature vectors so that an optimized access method can be applied. To incorporate human visual perception into our system,
we also conducted experiments that involved a number of subjects classifying images into different classes for neural network
training. We demonstrate that not only can our neural network system reduce the dimensions of the feature vectors, but that
the reduced dimensional feature vectors can also be mapped to an optimized access method for fast and accurate indexing.
Received 11 June 1998 / Accepted 25 July 2000 Published online: 13 February 2001 相似文献
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Watersnakes: energy-driven watershed segmentation 总被引:13,自引:0,他引:13
Hieu Tat Nguyen Worring M. van den Boomgaard R. 《IEEE transactions on pattern analysis and machine intelligence》2003,25(3):330-342
The watershed algorithm from mathematical morphology is powerful for segmentation. However, it does not allow incorporation of a priori information as segmentation methods that are based on energy minimization. In particular, there is no control of the smoothness of the segmentation result. In this paper, we show how to represent watershed segmentation as an energy minimization problem using the distance-based definition of the watershed line. A priori considerations about smoothness can then be imposed by adding the contour length to the energy function. This leads to a new segmentation method called watersnakes, integrating the strengths of watershed segmentation and energy based segmentation. Experimental results show that, when the original watershed segmentation has noisy boundaries or wrong limbs attached to the object of interest, the proposed method overcomes those drawbacks and yields a better segmentation. 相似文献
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Mining Projected Clusters in High-Dimensional Spaces 总被引:1,自引:0,他引:1
Bouguessa Mohamed Wang Shengrui 《Knowledge and Data Engineering, IEEE Transactions on》2009,21(4):507-522
Clustering high-dimensional data has been a major challenge due to the inherent sparsity of the points. Most existing clustering algorithms become substantially inefficient if the required similarity measure is computed between data points in the full-dimensional space. To address this problem, a number of projected clustering algorithms have been proposed. However, most of them encounter difficulties when clusters hide in subspaces with very low dimensionality. These challenges motivate our effort to propose a robust partitional distance-based projected clustering algorithm. The algorithm consists of three phases. The first phase performs attribute relevance analysis by detecting dense and sparse regions and their location in each attribute. Starting from the results of the first phase, the goal of the second phase is to eliminate outliers, while the third phase aims to discover clusters in different subspaces. The clustering process is based on the K-means algorithm, with the computation of distance restricted to subsets of attributes where object values are dense. Our algorithm is capable of detecting projected clusters of low dimensionality embedded in a high-dimensional space and avoids the computation of the distance in the full-dimensional space. The suitability of our proposal has been demonstrated through an empirical study using synthetic and real datasets. 相似文献
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Ruochen Liu Fei He Jing Liu Wenping Ma Yangyang Li 《Pattern Analysis & Applications》2014,17(3):633-654
To cluster data set with the character of symmetry, a point symmetry-based clonal selection clustering algorithm (PSCSCA) is proposed in this paper. Firstly, an immune vaccine operator is introduced to the classical clonal selection algorithm, which can gain a priori knowledge of pending problems so as to accelerate the convergent speed. Secondly, a point symmetry-based similarity measure is used to evaluate the similarity between two samples. Finally, both kd-trees-based approximate nearest neighbor searching and k-nearest-neighbor consistency strategy is used to reduce the computation complexity and improve the clustering accuracy. In the experiments, first of all, four real-life data sets and four synthetic data sets are used to test the performance of PSCSCA. PSCSCA is also compared with multiple existing algorithms in terms of clustering accuracy and convergent speed. In addition, PSCSCA is applied to a real-world application, namely natural image compression, with good performance being obtained. 相似文献
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文章介绍了Web文档聚类中普遍使用的基于分割的k-means算法,分析了k-means算法所使用的向量空间模型和基于距离的相似性度量的局限性,从而提出了一种改善向量空间模型以及相似性度量的方法。实验表明,改进后的k-means算法不仅保留了原k-means算法效率高的优点,而且具有更高的准确性。 相似文献