Description: The ICA/BSS algorithms are pure mathematical formulas, powerful, but rather mechanical procedures: There is not very much left for the user to do after the machinery has been optimally implemented. The successful and efficient use of the ICALAB strongly depends on a priori knowledge, common sense and appropriate use of the preprocessing and postprocessing tools. In other words, it is preprocessing of data and postprocessing of models where expertise is truly ne-Instruction :
The ICA/BSS algorithms are pure mathematical formulas, powerful, but rather mechanical procedures: There is not very much left for the user to do after the machinery has been optimally implemented. The successful and efficient use of the ICALAB strongly depends on a priori knowledge, common sense and appropriate use of the preprocessing and postprocessing tools. In other words, it is preprocessing of data and postprocessing of models where expertise is truly needed. Platform: |
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Author:孙占全 |
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Description: 该算法可以用vc实现Apriori算法的全部．请大家放心使用！-the algorithm can be used vc Apriori algorithm to achieve the full. Please rest assured use! Platform: |
Size: 405504 |
Author:小强强 |
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Description: 用c++编写的apriori算法，数据挖掘的经典算法，但需要扫描多次数据库，已逐渐被fpgrowth代替-prepared with the algorithm algorithms, data mining algorithms classic, but need to scan multiple databases, have gradually been replaced fpgrowth Platform: |
Size: 2073600 |
Author:linus yue |
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Description: 聚类分析技术有着广泛应用.因为在对图像进行聚类分析时,通常缺少可资利用的先验知识,所以需要采用无监督的聚类算法.为了适应图像检索的需要,提出了一种新型的无监督聚类方法,即根据离群点信息来自动确定聚类算法的终止时机.此方法还弥补了现有聚类算法在离群点识别、使用上的缺欠.为验证其可行性,用其改进了CURE和ROCK两个经典算法.实验表明,改进后的两个算法都能自动终止,并能取得优于以往的聚类效果. -clustering analysis techniques have wide application. In the image clustering analysis, usually available to the lack of a priori knowledge, Therefore, the need for unsupervised clustering algorithm. To meet the needs of image retrieval, propose a novel unsupervised clustering method, That is, according to information outliers automatically clustering algorithm to determine the time of termination. This method also makes up for in the existing clustering algorithm outliers identification, the use of the shortcomings. To test its feasibility. use its improved CURE ROCK and two classical algorithm. Experiments show that The two improved algorithm can automatically terminated and can be made better than the previous clustering effect. Platform: |
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Author: |
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Description: 基于Snake 模型的图像分割技术是近年来图像处理领域的研究热点之一。Snake 模型承载上层先验知识并融合
了图像的底层特征,针对医学图像的特殊性,能有效地应用于医学图像的分割中。本文对各种基于Snake 模型的改进算法和
进化模型进行了研究,并重点梳理了最新的研究成果,以利于把握基于Snake 模型的医学图像分割方法的脉络和发展方向。-Snake model based on image segmentation image processing technology in recent years one of the hot areas of research. Snake model bearing a priori knowledge of the upper and bottom of the convergence of image features, for the specificity of medical images can be effectively applied to medical image segmentation. In this paper, model-based Snake improved algorithm and evolutionary models were studied and focused on combing the latest research results, in order to grasp the Snake model based on medical image segmentation method of context and direction of development. Platform: |
Size: 476160 |
Author:赵希成 |
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Description: apriori算法的java代码，APRRORI算法使用频繁项性质的先验知识，逐层搜索迭代，用K-项集产生(K+1)-项集。APRRORI算法的一个显著特点是:利用APRIORI性质，压缩了频繁项集，提高了算法的效率。
-apriori algorithm java code, APRRORI algorithm uses the a priori nature of frequent itemsets knowledge, step by step iterative search using K-itemsets generated (K+ 1)- itemsets. APRRORI algorithm A significant feature is: the nature of the use of APRIORI, compression of the frequent itemsets, improve the efficiency of the algorithm. Platform: |
Size: 21504 |
Author:xinyuanwo |
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Description: 一个新的纹理合成的上行采样算法，即利用现有的高分辨率的样本纹理作为先验引导条件，对合
成的低分辨率的纹理进行插值，获取更好的高分辨率纹理合成结果．该算法的主要思想是基于联合双边滤波器进
行纹理合成上采样，对低分辨率合成纹理应用空间滤波，而将一相似的边界滤波联合地应用在高分辨率的样本纹
理上-Texture synthesis of a new sampling algorithm for the uplink, that is, use of the existing samples of high-resolution texture as a guide a priori conditions for the synthesis of low-resolution texture interpolation, to obtain better results of high-resolution texture synthesis. The main idea of the algorithm is based on the joint bilateral filter for texture synthesis on sampling, on the application of low-resolution texture synthesis of spatial filtering, and a similar use of joint border filtering samples in the high-resolution textures on the Platform: |
Size: 803840 |
Author:ws |
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Description: 该算法是经典的信噪比估计算法——最大似然估计算法，利用接收信道的先验概率密度函数，ML法能够很好的估计信号的信噪比-The algorithm is a classic signal to noise ratio estimation algorithm- maximum likelihood estimation algorithm, using the a priori receiver channel probability density function, ML method can be a very good signal to noise ratio is estimated Platform: |
Size: 1024 |
Author:贾小勇 |
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Description: Apriori算法【l】：1994年由R．Agrawal等人提出来的Apriori算法是
关联规则挖掘的一个经典算法，后来的许多算法都是基于该算法的思想。算
法的名称来源于在算法中应用了频繁项集的先验知识，即：一个频繁项集的
任一非空子集必定是频繁项集；因此只要某一项集是非频繁的，则其超集就
无须再检验。-Apriori algorithm】 【l: 1994 by R. Agrawal et al to the Apriori algorithm is a classical association rule mining algorithm, and later many of the algorithms are based on the idea of the algorithm. The name comes from the algorithm applied in the algorithm a priori knowledge of frequent item sets, ie: any of a frequent itemset must be a non-empty subset of frequent item sets so long as a particular set of non-frequent, its superset to no longer need to test. Platform: |
Size: 205824 |
Author:plairstar |
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Description: program code Apriori Algorithm (data mining) in Delphi. I found it after I read a book "Algoritma Data Mining". Apriori Algorithm is an influental algorithm for mining frequent itemsets for boolean association rules Platform: |
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Author:bwindhya |
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Description: A nonparametric adaptive filtering approach is proposed in this paper. The algorithm is
obtained by exploiting a time-varying step size in the traditional NLMS weight update
equation. The step size is adjusted according to the square of a time-averaging estimate
of the autocorrelation of a priori and a posteriori error. Therefore, the new algorithm has
more effective sense proximity to the optimum solution independent of uncorrelated measurement
noise. Moreover, this algorithm has fast convergence at the early stages of adaptation
and small final misadjustment at steady-state process. It works reliably and is easy
to implement since the update function is nonparametric. Furthermore, the experimental
results in system identification applications are presented to illustrate the principle and
efficiency of the proposed algorithm. Platform: |
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Author:mostafa |
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Description: 协同进化算法[I5] (Co-Evolutionary Algorithm, CEA)是研究者在协同进化理论基础
上提出的一类新算法。这类算法强调了种群与环境之间、种群与种群之间在不断进化过
程中的协调。与传统进化算法相比较,CEA可以对待求问题解空间进行合理的种群划分,
对较大规模的问题求解能有效跳出局部最优点,寻找到更好的优化解虽然CEA研
究起步较晚,但由于它的优越性,目前己成为当前进化计算的一个研究热点。
-Existing coevolutionary techniques can be divided into two
main classes: competitive coevolution and cooperative coevolution. Regardless of the approach adopted, the design of coevolutionary algorithms for MO optimization requires one to address many issues that are unique to the MO problems. In this
aspect, insights such as incorporation of various elitist and diversity mechanisms obtained from the design of MOEAs can
be similarly exploited in the design of MOCAs. On the other
hand, successful implementation of coevolution requires one to
consider various design issues [49], such as problem decomposition, handling of parameter interactions, and credit assignment. The issues of problem decomposition and parameter interactions are often problem dependent, and the approaches for
solving these issues may not be knowna priori. These factors
motivated the work for an alternative coevolutionary model presented in this paper. Platform: |
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Author:王朝 |
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Search - priori algorithm