In the context of machine learning, DKL(P‖Q) is often called the information gain achieved if P is used instead of Q. 即如果用P来描述目标问题,而不是用Q来描述目标问题,得到的信息增量。 在机器学习中,P往往用来表示样本的真实分布,比如[1,0,0]表示当前样本属于第一类。Q用来表示模型所预测的分布...
通过前面的讨论,我们可以感觉到,当 cross-entropy 和 KL 散度作为分类模型的损失函数时,从模型初始的损失值,到模型最优时的损失值,这两种损失函数的减少量是相同的,所以从这个角度讲,cross-entropy 与 KL 散度的作用是相同的。 4. cross-entropy 优化分类模型 cross-entropy作为损失函数常常被用来优化分类模型。采...
The cross-entropy loss function is used to find the optimal solution by adjusting the weights of a machine learning model during training. The objective is to minimize the error between the actual and predicted outcomes. A lower cross-entropy value indicates better performance. If you’re familiar...
几乎是entropy的两倍了. 换句话说, 气象站平均发出4.58的信息, 但是只有2.23的信息是有用的. 这是因为我们所使用的编码方式是建立在一定的假设的. 例如, 当我们使用2bit message在晴天的时候, 平均来说每4(2的2次方)天会有一天是晴天, 换句话说, 我们在预测晴天的概率是25%, 如果假设不成立,...
cross entropy 损失函数:(^yy^为预测值,yy为真实值) −ylog^y−(1−y)log(1−^y)−ylogy^−(1−y)log(1−y^) 直观解释 简单点的解释是,logistic regression 时,证明两个凸函数相加还是凸函数,因为yy不是 0 就是 1,那就要证明此时−log^y−logy^和−log(1−^y)...
binary cross-entropy和categorical cross-entropy是相对应的损失函数。 对应的激活函数和损失函数相匹配,可以使得error propagation的时候,每个输出神经元的“误差”(损失函数对输入的导数)恰等于其输出与ground truth之差。 (详见Pattern Recognition and Machine Learning一书5.3章)...
The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation, and Machine Learning. This article reviews the book "The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation, and Machine Learning," b... Blossom,Paul - 《...
在第2步中,我们通常会见到多种损失函数的定义方法,常见的有均方误差(error of mean square)、最大似然误差(maximum likelihood estimate)、最大后验概率(maximum posterior probability)、交叉熵损失函数(cross entropy loss),下面我们就来理清他们的区别和联系。一般地,一个机器学习模型选择哪种损失函数,是凭借经验而...
(2006). The Cross-Entropy Method: A Unified Approach to Combinatorial Optimiza- tion, Monte-Carlo Simulation, and Machine Learning (cit. on p. 64).The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation, and Machine Learning[J] . Lih-Yuan Deng....
In this paper we present a variation of the Cross Entropy method that can be applied on Dynamic Bayesian Networks for efficient learning of the model parameters. We demonstrate the results achieved on real world video streams using a variety of DBNs. Finally we compare this approach to the trad...