# by movie on 2019/12/18 import matplotlib.pyplot as plt import numpy as np from skimage import measure import cv2 # import the necessary packages def mse(imageA, imageB): # the 'Mean Squared Error' between the two images is the # sum of the squared
multiple r squared adjusted r squared http://web.maths.unsw.edu.au/~adelle/Garvan/Assays/GoodnessOfFit.html Goodness-of-Fit Statistics Sum of Squares Due to Error This statistic measures the total deviation of the response values from the fit to the
前言 论文“Reducing the Dimensionality of Data with Neural Networks”是深度学习鼻祖hinton于2006年发表于<SCIENCE >的论文,也是这篇论文揭开了深度学习的序幕. 笔记 摘要:高维数据可以通过一个多层神经网络把它编码成一个低维数据,从而重建这个高维数据,其中这个神经网络的中间层神经元数是较少的,可把这个神经网络叫做自动编码网络或自编码器(autoencoder).梯度下降法可用来微调这个自动编码器的权值,但是只有在初始化权值
SAD(Sum of Absolute Difference)=SAE(Sum of Absolute Error)即绝对误差和 SATD(Sum of Absolute Transformed Difference)即hadamard变换后再绝对值求和 MAD(Mean Absolute Difference)=MAE(Mean Absolute Error)即平均绝对差值 SSD(Sum of Squared Difference)=SSE(Sum of Squared Error)即差值的
TSS: Total Sum of Squares(总离差平方和) --- 因变量的方差 RSS: Residual Sum of Squares (残差平方和) --- 由误差导致的真实值和估计值之间的偏差平方和(Sum Of Squares Due To Error) ESS: Explained Sum of Squares (回归平方和) --- 被模型解释的方差(Sum Of Squares Due To Regression) TSS=RSS+ESS R2: Coefficien