上述三个损失函数在计算bounding box regression loss时,是独立的求4个点的loss,然后相加得到最终的损失值,这种做法的前提是四个点是相互独立的,而实际上是有一定相关性的 实际评价检测结果好坏的指标是IoU,这两者是不等价的,多个检测框可能有相同的loss,但IoU差异很大 IoU Loss IoU loss的定义如下: 其中P代表预测...
Example: loss functions in linear regression In order to introduce loss functions, we use the example of alinear regression model where is the dependent variable, is avectorof regressors, is a vector of regression coefficients and is an unobservable error term. Estimation losses Suppose that we u...
Understand the significance of loss functions in deep learning by knowing their importance, types, and implementation along with the key benefits they offer. Read on
Evaluation of Different Heuristics for Accommodating Asymmetric Loss Functions in RegressionMost machine learning methods used for regression explicitly or implicitly assume a symmetric loss function. However, recently an increasing number of problem domains require loss functions that are......
3. Log Loss看形式我们基本可以猜测是从概率的方向得到的;看过经典斯坦福的ML课程的同学都知道,先是讲 linear regression 然后引出最小二乘误差,之后概率角度高斯分布解释最小误差。然后讲逻辑回归,使用MLE来引出优化目标是使得所见到的训练数据出现概率最大。
这里有个PDF可以参考一下:Lecture 6: logistic regression.pdf. 二、平方损失函数(最小二乘法, Ordinary Least Squares ) 最小二乘法是线性回归的一种,OLS将问题转化成了一个凸优化问题。在线性回归中,它假设样本和噪声都服从高斯分布(为什么假设成高斯分布呢?其实这里隐藏了一个小知识点,就是中心极限定理,可以...
Below is an image showing a list of different types of loss functions for Classification and Regression tasks. Source:Heartbeat Nisha Aryais a Data Scientist and Freelance Technical Writer. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge ...
不同的loss函数,具有不同的拟合特性。对于第一项Loss函数,如果是Square loss,那就是最小二乘;如果是Hinge Loss,那就是著名的SVM;如果是exp-Loss,那就是 Boosting;如果是log-Loss,那就是Logistic Regression;等等。 loss函数一般都是通过mle推导出来的。使用最大似然来导出代价函数的方法的一个优势是,它减轻了为...
Cross entropy and mean-squared error (MSE) are two standard loss functions in regression tasks, whereas other supervised regression loss functions are also open. The MSE loss function is shown in Eq. (4.15), where ϕ presents the task-specific parameters: (4.15)LTifϕ=∑xj,yj~Tifϕxj...
http://www.ics.uci.edu/~dramanan/teaching/ics273a_winter08/lectures/lecture14.pdf(题名“Loss functions; a unifying view”)。 一、损失项 对回归问题,常用的有:平方损失(for linear regression),绝对值损失; 对分类问题,常用的有:hinge loss(for soft margin SVM),log loss(for logistic regression)。