Image Processing and Jump Regression Analysis builds a bridge between the worlds of computer graphics and statistics by addressing both the connections and the differences between these two disciplines. The author provides a systematic breakdown of
A class of nonparametric smoothing kernel methods for image processing and filtering that possess edge-preserving pro- perties is examined. The proposed approach is a nonlinearly modified version of the classical nonparametric regression estimates uti
The emphasis of this book is on the practice of regression and analysis of variance. The objective is to learn what methods are available and more importantly, when they should be applied.
function [weights] = regression(train_data_X, train_data_Y) %JUST ONE LINE OF CODE weights = inv(train_data_X'*train_data_X)*train_data_X'*train_data_Y; return;
