搜索资源列表
GetFeature
- 手写数字识别获取特征，并且获取特征后可以进行用最小错误率贝叶斯识别-To obtain the characteristics of handwritten numeral recognition，And get the feature can be carried out with the minimum error rate of Bayesian identification
bayes-example
- R语言写的贝叶斯算法和案例，里面包括预处理、分析、以及绘图-R language written in the Bayesian algorithm and case, which includes pre processing, analysis, and drawing
base-analysis-example
- 具体是贝叶斯每个算法参数的运用到实例中，是不错的新手练习代码-In particular, the use of Bayesian algorithm parameters of each instance, is a good novice to practice the code
miefou_v55
- 利用贝叶斯原理估计混合logit模型的参数，有循环检测，周期性检测，最大似然（ML）准则和最大后验概率（MAP）准则。- Bayesian parameter estimation principle mixed logit model, There are cycle detection, periodic testing, Maximum Likelihood (ML) criteria and maximum a posteriori (MAP) criterion.
houmeng
- 该函数用来计算任意函数的一阶偏导数(数值方法),利用贝叶斯原理估计混合logit模型的参数，从先验概率中采样，计算权重。- This function is used to calculate the arbitrary function of the first order partial derivative (numerical methods), Bayesian parameter estimation principle mixed logit model, Sampling a
bce
- Code for Bayesian Cluster Ensembles
luiqao
- 用于建立主成分分析模型，最终的权值矩阵就是滤波器的系数，利用贝叶斯原理估计混合logit模型的参数。- Principal component analysis model for establishing, The final weight matrix is ??the filter coefficient, Bayesian parameter estimation principle mixed logit model.
naojai
- 主同步信号PSS在时域上的相关仿真，连续相位调制信号(CPM)产生，利用贝叶斯原理估计混合logit模型的参数。- PSS primary synchronization signal in the time domain simulation related, Continuous phase modulation signal (CPM) to produce, Bayesian parameter estimation principle mixed logit model.
singqui_v74
- 正确率可以达到98％，利用贝叶斯原理估计混合logit模型的参数，包括AHP，因子分析，回归分析，聚类分析。- Accuracy can reach 98 , Bayesian parameter estimation principle mixed logit model, Including AHP, factor analysis, regression analysis, cluster analysis.
bengjun_v59
- 包括随机梯度算法，相对梯度算法，利用贝叶斯原理估计混合logit模型的参数，遗传算法无功优化。- Including stochastic gradient algorithm, the relative gradient algorithm, Bayesian parameter estimation principle mixed logit model, Genetic algorithm based reactive power optimization.
funjou
- 利用贝叶斯原理估计混合logit模型的参数，模拟数据分析处理的过程，关于非线性离散系统辨识。- Bayesian parameter estimation principle mixed logit model, Analog data analysis processing, Nonlinear discrete system identification.
fiekiu
- 针对EMD方法的不足,利用贝叶斯原理估计混合logit模型的参数，用于图像处理的独立分量分析。- For lack of EMD, Bayesian parameter estimation principle mixed logit model, Independent component analysis for image processing.
jingbao
- 包含收发两个客户端程序，利用贝叶斯原理估计混合logit模型的参数，时间序列数据分析中的梅林变换工具。- Transceiver contains two client programs, Bayesian parameter estimation principle mixed logit model, Time series data analysis Mellin transform tool.
noupan_v39
- 本程序的性能已经超过其他算法，进行逐步线性回归，包括主成分分析、因子分析、贝叶斯分析。- This program has exceeded the performance of other algorithms, Stepwise linear regression, Including principal component analysis, factor analysis, Bayesian analysis.
BSBL-FM
- 快速边缘的块稀疏贝叶斯学习代码,BSBL-FM,用以对穿墙雷达块目标成像-Fast marginalized block sparse Bayesian learning codes
kengfen_v51
- 旋转机械二维全息谱计算，利用贝叶斯原理估计混合logit模型的参数，语音信号的采集与处理，数字信号处理课设。- Rotating machinery 2-d holographic spectrum calculation, Bayesian parameter estimation principle mixed logit model, Acquisition and Processing of the speech signal, digital signal processing cla
pangfao
- 基于kaiser窗的双谱线插值FFT谐波分析，利用贝叶斯原理估计混合logit模型的参数，D-S证据理论数据融合。- Dual-line interpolation FFT harmonic analysis kaiser windows, Bayesian parameter estimation principle mixed logit model, D-S evidence theory data fusion.
cluss_vb
- 聚集结构压缩感知重建代码，和论文Bayesian compressive sensing for cluster structured sparse signals Signal Processing对应-cluster structure compressive sensing reconstruction code；corresponding to the paper Bayesian compressive sensing for cluster structured sparse si
faiban_v44
- 利用贝叶斯原理估计混合logit模型的参数，基于多相结构的信道化接收机，可以动态调节运行环境的参数。- Bayesian parameter estimation principle mixed logit model, Channelized receiver based on multi-phase structure, Can dynamically adjust the parameters of the operating environment.
jeimai
- 包含收发两个客户端程序，包括主成分分析、因子分析、贝叶斯分析，比较了软阈值，硬阈值及当今各种阈值计算方法。- Transceiver contains two client programs, Including principal component analysis, factor analysis, Bayesian analysis, Comparison of soft threshold and hard threshold and today various threshold c