文件名称：General loss functions
文件大小：377KB
文件格式：PDF
更新时间：2021-08-31 04:45:06
AI
General loss functions Building off of our interpretations of supervised learning as (1) choosing a representation for our problem, (2) choosing a loss function, and (3) minimizing the loss, let us consider a slightly more general formulation for supervised learning. In the supervised learning settings we have considered thus far, we have input data x ∈ Rn and targets y from a space Y. In linear regression, this corresponded to y ∈ R, that is, Y = R, for logistic regression and other binary classification problems, we had y ∈ Y = {−1, 1}, and for multiclass classification we had y ∈ Y = {1, 2, . . . , k} for some number k of classes.