jun1-cs 님의 블로그

ensemble learning: compare multiple classifer models and make better decision to reduce the prediction error 1. Independent ensemble learning: ml models operate in parallel -> fast1) voting: train multiple type of models with train data and vote to classify itEx) plurality voting, majority voting- we can also mathematically prove that n classifiers with same error rate e can exhibit lower or sam..

1. Basic concepts1) line and segmenttwo points x1, x2 -> y=wx1 + (1-w)x2for 0for w1: y is line 2) affine set and convex setaffine set: the set C that contains line wx1 + (1-w)x2 or, w1x1 + w2x2 + w3x3 +...+ wnxn (Sigma(wi)=1)-> affine combination: (w1,w2,...,wn) that belong to set C -> meaning: combination of weights that make y lies on x1~xn points convex set: the set C that contains segment wx..