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ensemble-average
- Generation of the ensemble average.
R36clusterings
- function in R software to calculate 36 clusterings of a data matrix (clusterings are in columns), using hierarchical average, cosine, centroid and single linkages, pam and neural gas and applying the Euclidean distance to the data. Clusterings can
ensemble-toolbox
- a very useful toolbox for classifiers combination: majority voting, max, min, average mesures and others
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
2D-Lattice-Percolation
- 2D-Lattice-Percolation,and ensemble average
sentiment_analysis_on_online_customer_reviews
- The project is to determine how much a particular factor influences on the helpfulness of a review. We extracted features like polarity, rating, average word length, helpfulness ratio the collected amazon data. We used gradient boosting classifie
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