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opticalflow
- this program, opticalflow.c, is an implementation of Uras et al. 1988 s motion-this program, opticalflow.c. is an implementation of Uras et al. 1,988 s motion
Threeway.tar
- Tucker, PARAFAC, GRAM, RAFA and misc. 2nd order models with a test data set (old version now covered by the N-way Toolbox)-Tucker, B6, GRAM, RAFA and misc. 2nd order models with a test's data et (old version now covered by the N-way Toolbox )
nca
- 功能为neighborhood components analysis，a quick matlab implementation of NCA (see Goldberger et al, NIPS04).
免疫算法
- In the last twenty years, the design of efficient function optimizers has been a crucial topic of research work. Many theoretical and practical research problems involve combinatorial optimization, which is obtaining the optimal solution among a fini
apriori
- A program to find association rules and frequent item sets (also closed and maximal) with the apriori algorithm (Agrawal et al. 1993), which carries out a breadth first search on the subset lattice and determines the support of itemsets by subset tes
mfa_sy2
- 根据Yan et al. 文章编写的MFA程序，希望对大家有帮助；如果存在问题，也希望指正！-According to Yan et al. The article written MFA program you want to help If there are problems, but also want to correct me!
sa-ppt-sample
- 模拟退火算法最早的思想由Metropolis等（1953）提出，1983年Kirkpatrick等将其应用于组合优化。-Simulated annealing algorithm was first thought by Metropolis et al (1953) suggested that, in 1983, Kirkpatrick and so on will be applied to combinatorial optimization.
apriori
- 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
Pulsed-Neural-Networks
- Neurons use action potentials to signal over long distances, as summarized in Chapter 1 by Gerstner. The all-or-none nature of the action potential means that it codes information by its presence or absence, but not by its size or shape. In this
ReviewBees-Hamdi-et-al
- this a review of bee based clustering algorithm
mean-shift
- Mean Shift 这个概念最早是由Fukunaga等人[1]于1975年在一篇关于概率密度梯度函数的估计中提出来的,其最初含义正如其名,就是偏移的均值向量,在这里Mean Shift是一个名词,它指代的是一个向量,但随着Mean Shift理论的发展,Mean Shift的含义也发生了变化,如果我们说Mean Shift算法,一般是指一个迭代的步骤,即先算出当前点的偏移均值,移动该点到其偏移均值,然后以此为新的起始点,继续移动,直到满足一定的条件结束.-Mean Shift concept
MachinePLearning[TomPM.PMitchell]
- 学习机器学习的好书。Tom.M.Mitchell著,中文版,由曾华军等人翻译此书非常好,把BP原理讲得非常透彻,由其是误差的公式推导,比别的书都说得清楚一点-Learning machine learning books. Tom.M.Mitchell, Chinese edition, by Ceng Huajun et al. Translate the book is very good, the principle of BP speaks very thorough, the erro
Cappe-2007-An-Overview-of-Exist
- 这是一篇很好的有关粒子滤波的综述性论文，具有很高的参考价值。-It is now over a decade since the pioneering contribution of Gordon et al. (1993), which is commonly regarded as the first instance of modern sequential Monte Carlo (SMC) approaches. Initially focussed on applicat
duoquanzhishengjingwangluo
- 应用多权值神经网络方法对静态手势进行识别, 对手势字母图像采用傅里叶描述子提取特征信息, 取低频信息成分构建成犯维特征向量, 并应用多权值神经网络的算法, 构建各类的神经元网络-W ith th e develo Pm en t of hu m an eom p uter intera etion te ehn olo盯, th e h as been b ased on an im P o rt a n t tas k fo r U r o n s diseu ssion
tp1
- nombre parfait et factoriel
fsm_release
- 由王兴刚、白翔等人发表于2012CVPR上的文章，Fan Shape Model for Object Detection， 构建了全新的模型用于物体检测，师兄的这个组工作一直做得不错，赞一个！-The article from PROCEEDINGS, White Xiang et al published in 2012CVPR on, Fan Shape Model for Object Detection, build a new model for object detection,
KNNweka
- application de l algorithme KNN en utilisant WEKA dans la lecture des fichier et une methodes optimisé pour parcourir la liste des instance une seul fois lors de la recherche et de la classification application of KNN algorithm in WEKA using file
RBF
- Radial Basis Function interpolation-RBF was originally developed for scattered multivariate data interpolation (Hardy 1971). It uses a series of basis functions that are symmetric and centred at each sampling point. Radial basis functions are a s
1709.04326
- 多智能体设置在机器学习中的重要性日益突出。超过了最近的大量关于深度的工作多agent强化学习，层次强化学习，生成对抗网络和分散优化都可以看作是这种设置的实例。然而，多学习代理人的存在这些设置使得培训问题的非平稳常常导致不稳定的训练或不想要的最终结果。我们提出学习与对手的学习意识（萝拉），一种方法，原因的预期。其他代理的学习。罗拉学习规则包括一个额外的术语，解释了在预期的参数更新的代理政策其他药物。我们发现，利用似然比策略梯度更新的方法，可以有效地计算萝拉更新规则，使该方法适合于无模型强化学习。这
Aja_Kick
- this si si th et ehsf hat o