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UMLCours2012
- Un diagramme de cas présente un ensemble d acteurs et de cas d utilisation avec leurs relations
flashftp
- sff454序列文件 提取 拼接 程序 主要用于序列文件中congtig的拼接-sff ensemble
Neural-Network-Ensemble
- 神经网络集成,集成了最近几年一些关于神经网络发展的文献,适合深入了解的筒子学习-Neural network integration, integration in recent years some of the literature on the development of neural networks, suitable for in-depth understanding of the cheese learning
EIH-model
- 就超音速飞行体产生的激波脉冲识别展开研究,分析丁目标信号的脉冲特性和基于EIH (Ensemble Interval Histogram)模型的波形特征提取方法.分析了EIH模型在波形结梅特征提取的过程中存在的问题,提出了一种自适应EIH模型.-Supersonic shock pulse recognition study, analysis of the signal pulse characteristics of the small target and EIH (Ensemble
ensemble-learning
- 主要讲述深度学习的一篇很有用很有用的论文,是最初提出来的,很好,很有帮助-Focuses on the depth of learning a useful useful papers, initially proposed, very good, very helpful
Ensemble
- javafx的例子代码,有兴趣学习的话是很好的学习资料,这是一个多个图像变换的例子-javafx example code, learning is a good learning materials, which is more than one image transformation example
knn
- une petite application qui utilise l algorithme KNN (KPPV) en utilisant le fichier iris comme l ensemble initial a small application that uses the KNN algorithm (kNN) file using the iris as the initial set
BDD-vue-densemble
- base de données vue d ensemble
computerwork_2
- 2. 设 是窄带信号,定义 是在 区间上均匀分布的随机相位。 是寬带信号,它是一个零均值、方差为1的白噪音信号e(n)激励一个线性滤波器而产生,其差分方程为 。 1) 计算 和 各自的自相关函数,并画出其函数图形。根据此选择合适的延时,以实现谱线增强。 2) 产生一个 序列。选择合适的 值。让 通过谱线增强器。画出输出信号 和误差信号e(n)的波形,并分别与 和 比较。 -Computer Experiments: 1. Consider an AR process x
simulation
- traitement d ensemble d image (signature,piece mecanique )
AC
- ac字符匹配算法--经典的多模式匹配算法,可以保证对于给定的长度为n的文本,和模式集合P{p1,p2,...pm},在O(n)时间复杂度内,找到文本中的所有目标模式,而与模式集合的规模m无关-ac character matching algorithm- classic multi-pattern matching algorithm can ensure that the text of n for a given length, and the mode set P {p1, p2, ..
tpcompilll1
- Dons ce TP nous avons réalisé un analyseur syntaxique en utilisant la méthode d’analyse descendant LL1. Cette méthode permet au premier temps de tester si une grammaire quand veut analyser si elle est non récursive à gauche et factorisé. Ensui
01._THE_MULTI-DIMENSIONAL_ENSEMBLE_EMPIRICAL_MODE
- A multi-dimensional ensemble empirical mode decomposition (MEEMD) for multi dimensional data (such as images or solid with variable density) is proposed here. The decomposition is based on the applications of ensemble empirical mode decomposition
clusterensemble
- 聚类集成学习方法,提高算法的健壮性,聚类效果比一般算法好-Clustering ensemble learning methods to improve the robustness of the algorithm, the clustering effect is better than the general algorithm
ClusterEnsembleTest
- 聚类集成测试,让你迅速明白聚类集成是怎么回事-Clustering integration testing, you quickly understand cluster ensemble is how it
Cluster
- 聚类集成学习方法,提高算法的健壮性,聚类效果比一般算法好-Clustering ensemble learning methods to improve the robustness of the algorithm, the clustering effect is better than the general algorithm
semigrand_698F
- Semi-grand ensemble simulations and Gibbs-Duhemintegration
code
- 利用多特征以及随机子空间的分子显形识别 Phenotype Recognition with Combined Features and Random Subspace Classifier Ensemble, published on Bioinformatics 20-Phenotype Recognition with Combined Features and Random Subspace Classifier Ensemble, published on Bioinformati
[emuch.net][676077]gcmc
- 分子模拟学习的好材料,巨正则系综montecarlo-Molecular modeling good learning materials, the grand canonical ensemble montecarlo
pca
- Function to perform Principle Component Analysis over a set of training vectors passed as a concatenated matrix. Usage:- [V,D,M] = pca(X,n) [V,D] = pca(X,aM,n) where:- <input> X = concatenated set of column vectors