【摘要】 监督学习中的损失函数常用来评估样本的真实值和模型预测值之间的不一致程度,一般用于模型的参数估计。受应用场景、数据集和待求解问题等因素的制约,现有监督学习算法使用的损失函数的种类和数量较多,而且每个损失函数都有各自的特征,因此从众多损失函数中选择适合求解问题最优模型的损失函数是相当困难的。研究了监督学习算法中常用损失函数的标准形式、基本思想、优缺点、主要应用以及对应的演化形式,探索了它们适用的应用场景和可能的优化策略。本研究不仅有助于提升模型预测的精确度,而且也为构建新的损失函数或改进现有损失函数的应用研究提供了一个新的思路。更多还原

【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.更多还原