搜索资源列表
arimanet
- ARIMA模型全称为自回归积分滑动平均模型(Autoregressive Integrated Moving Average Model,简记ARIMA)，是由博克思(Box)和詹金斯(Jenkins)于70年代初提出一著名时间序列预测方法[1] ，所以又称为box-jenkins模型、博克思-詹金斯法。其中ARIMA（p，d，q）称为差分自回归移动平均模型，AR是自回归， p为自回归项； MA为移动平均，q为移动平均项数，d为时间序列成为平稳时所做的差分次数。所谓ARIMA模型，是指将非平稳
Objctioforsmage
- Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years(The best-performing methods are complex ensemble systems that typically combine multiple low-level image features with high-level conte
CEEMD
- 法国人改进EMD的新程序，包含了EMD，EEMD，CEEMDAN算法，并含有具体例子(A complete ensemble empirical mode decomposition with adaptive noise.pdf)
EnsembleSVM-master
- ensemble learning in SVM
EnKF集合卡尔曼滤波代码
- EnKF集合卡尔曼滤波代码，用于读写模式集合，包括两种扰动观测分析方案。(These files illustrate the model used for storing, reading and writing the ensemble of model states.)
lrrfgbdtxgboost
- 用Xgboost作为集成算法，将LR，RF,GBDT三个分类器的结果综合起来。(Using Xgboost as an ensemble algorithm, we synthesize the results of three classifiers of LR, RF and GBDT.)
数据挖掘 R语言实战-代码
- 数据挖掘算法R语言实现，包括聚类、判别、集成学习、随机森林、神经网络、支持向量机等算法。(Data mining algorithm R language implementation, including clustering, discrimination, ensemble learning, random forest, neural network, support vector machines and other algorithms.)
iForest-master
- 孤立森林 是一个基于Ensemble的快速异常检测方法，具有线性时间复杂度和高精准度，是符合大数据处理要求的state-of-the-art算法(Isolated forest is a fast anomaly detection method based on Ensemble. It has linear time complexity and high precision. It is a state-of-the-art algorithm that meets the require
EEMD处理
- EMD的扩展算法，集合经验模态分解(EEMD)， 程序完整可运行(Extension algorithm of EMD, ensemble empirical mode decomposition (EEMD), complete and runnable program)
PyEMD-master
- CEEMDAN in Python This is what happened with Complete Ensemble Empirical Mode Decompostion with Adaptive Noise.
改进空域滤波
- 集合经验模态分解算法和基于负熵最大的Fast ICA算法对心磁信号的去噪处理(Ensemble empirical mode decomposition algorithm and Fast ICA algorithm based on maximum negative entropy for denoising processing of magnetic heart signals)