Loss function and application research in supervised learning

Abstract

The loss function in supervised learning is often used to evaluate the degree of inconsistency between the real value of the sample and the predicted value of the model,and is generally used for parameter estimation of the model.Due to the constraints of application scenarios,data sets and problems to be solved,there are many kinds and quantities of loss functions used by existing supervised learning algorithms,and each loss function has its own characteristics.Therefore,it is very difficult to select a loss function suitable for solving the optimal model of the problem from many loss functions.The standard forms,basic ideas,advantages and disadvantages,main applications and corresponding evolution forms of commonly used loss functions in supervised learning algorithms were studied,and their more appropriate application scenarios and possible optimization strategies were summarized.This study not only helps to improve the accuracy of model prediction,it also provides a new idea for the application of new loss functions or to improve the application of existing loss functions.

Keywords

• supervised learning;loss function;similarity measure