搜索资源列表
hong_enhancement
- 指纹图像增强，gabor滤波器，包括形态学分割，形态学细化，加窗短时傅里叶变换增强，方向图估计及平滑，纹线频率估计及平滑-Fingerprint image enhancement, gabor filters, normalization, equalization, morphological segmentation, morphological thinning, windowed short time Fourier transform enhanced estimates and s
matlab-econometric-toolbox
- “Applied Econometrics using MATLAB”配套的计量经济Matlab包-MATLAB code for: 1. least-squares, simultaneous systems (2SLS,3SLS, SUR) 2. limited dependent variable (logit, probit, tobit) and Bayesian variants 3. time-series (VAR, BVAR, ECM) estim
Ridge-Distance-Estimation-in-Fingerprint-Images-A
- IN THIS WE ARE STUDING Ridge Distance Estimation in Fingerprint Images Algorithm and Performance Evaluation
affintpoints
- 仿射不变Harris, Laplacian, det(Hessian) and Ridge 特征点检测 参考文献：An affine invariant interest point detector , K.Mikolajczyk and C.Schmid, ECCV 02, pp.I:128-142.-Matlab code for detecting Affine spatial interest points. Includes Harris, Laplacian, det(Hess
spatial-econometric
- 适用于空间计量的各种模型，包括SVAR SEM SMD 等，以及各种检验，如LM Walds等-least-squares, simultaneous systems (2SLS,3SLS, SUR) limited dependent variable (logit, probit, tobit) and Bayesian variants time-series (VAR, BVAR, ECM) estimation and accompanying forecasting func
Tikhonov_regularization_toolbox
- Tikhonov正则化工具箱，可实现病态方程组的正则化，以及采用L曲线法、岭估计法、GCV法等确定正则化参数，内含使用方法，亲测有效。-Tikhonov regularization toolbox, which can realize regularization in morbid equations, and using the L curve method, ridge estimation, GCV method to determine the regularization para
区域网平差
- 放射变换光束法平差；无控制点岭估计，一个控制点平移，两个平移+漂移；三个以上仿射变换(The radiation transform adjustment, the zero point ridge estimation, a control point translation, two control points translation + drifts, and more than three control points affine transformations)
ridge regression1
- 岭回归(英文名：ridge regression, Tikhonov regularization)是一种专用于共线性数据分析的有偏估计回归方法，实质上是一种改良的最小二乘估计法，通过放弃最小二乘法的无偏性，以损失部分信息、降低精度为代价获得回归系数更为符合实际、更可靠的回归方法，对病态数据的拟合要强于最小二乘法。 总之，本文档是岭回归的R语言实现代码，主要用于解决当模型中出现多重共线性问题，尤其是当你所有的解释变量都很重要，又无法通过其他检验来删除时，岭回归是一个很好的解决办法。(Ridge
AE5336-Fall2017-Proj2
- Some examples of monte carlo methods and basic estimation problems