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UMDA. a kind of estimation of distribution algorithm , which is the improvement of genetic algorithm-UMDA. A kind of estimation of distribution algorithm, which is the improvement of genetic algorithm
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用java实现遗传算法、进化算法、退火算法、分布估计算法、爬山算法、随机采样算法等算法-Use Java implement genetic algorithms, evolutionary algorithm, annealing algorithm, distribution estimation algorithm, climbing algorithms, random sampling algorithm of the algorithm.
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分布估计算法（EDA）讲义，29页PPT
1. 从GA到 EDA
2. 链接学习与模型
3. 连续EDA
4. EDA 与玻耳兹曼分布
5. 资源
-estimation of distribution algorithm（EDA）tutorial
1. from the GA to the EDA
2. Linking learning and models
3. Continuous EDA
4. EDA with Boltzmann distri
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分布估计算法的PBIL算法源码。新型的进化算法。-The resource of the PBIL algorithm among Estimation of Distribution Algorithms is uploaded. Estimation of Distribution Algorithms is a novel type of evolutionary algorithms.
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静态K-S检验下的copula 分布估计算法边缘分布的研究- Marginal distribution in copula estimation of distribution algorithm based static K-S test
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K近邻（KNN）：分类算法KNN是non-parametric分类器（不做分布形式的假设，直接从数据估计概率密度），是memory-based learning KNN不适用于高维数据（curse of dimension）-K-Nearest Neighbor (KNN): Classification Algorithm. KNN is a non-parametric classifiers (not to assume that the distribution of forms, fr
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分布估计算法优秀硕士论文，详细介绍了算法的过程及参数选择，适合初学者学习该算法-Estimation of distribution algorithms outstanding master thesis, introduces in detail the process and parameter selection algorithm, suitable for beginners to learn the algorithm,
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Estimation of Distribution Algorithm, 简称EDA,提出了一种全新的进化模式-Estimation of Distribution Algorithm
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分布估计算法应用于解决流水车间调度模型是的研究代码-Estimation of distribution algorithm is applied to solve the flow shop scheduling model is the code for research
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LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
Since version 2.8, it implements an SMO-ty
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libsvm-3.22.rar LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
Since version 2.8, it impl
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