因此损失代价函数为: 3.算法(algorithm): 现在我们的目的就是求解出一个使得代价函数最小的W: a.矩阵满秩可求解时(求导等于0): b.矩阵不满秩时(梯度下降): 梯度下降算法是一种求局部最优解的方法,对于F(x),在a点的梯度是F(x)增长最快的方向,那么它的相反方向则是该点下降最快的方向,具体参考wikipedia。 原
Unsupervised learning 无监督学习 others: Reinforcement learning ,recommender systems tools for machine learning ; experience is important 2.supervised learning “right answers”given supervised learning:数据集中的每个数据都是正确的答案 Regression Question : predict continuous valued output (Regression Ques...
一元线性回归(Simple Linear Regression): 假设只有一个自变量x(independent variable,也可称为输入input, 特征feature),其与因变量y(dependent variable,也可称为响应response, 目标target)之间呈线性关系,当然x和y之间不会完全是直线关系,而是会有一些波动(因为在现实中,不一定只有一个自变量x会影响因变量y,可能还会...
一元线性回归(Simple Linear Regression): 假设只有一个自变量x(independent variable,也可称为输入input, 特征feature),其与因变量y(dependent variable,也可称为响应response, 目标target)之间呈线性关系,当然x和y之间不会完全是直线关系,而是会有一些波动(因为在现实中,不一定只有一个自变量x会影响因变量y,可能还会...
三、Robust regression鲁棒线性回归(Laplace/Student似然+均匀先验) 因为先验服从均匀分布,所以求鲁棒线性回归即求Laplace/Student最大似然。在heavy tail(奇异点较多)情况下用鲁棒线性回归,因为Laplace/Student分布比高斯分布更鲁棒。 似然函数为: 由于零点不可微,所以求解析解困难,无法使用梯度下降法。引入Huber损失函数解...
训练机器其实是训练算法(train the algorithm)。算法是解决一个问题的方法,其中含有许多未定参数,用大数据训练机器就是一个确定参数值的过程。 现在回来说说开篇的监督与非监督。人类学习要有学习对象,或者学习资源,比如书籍和视频。机器学习也要有学习资源,即输入的数据(input),最终输出学习的成果(output)。
Linear regression is perhaps one of the most well known and well understood algorithms in statistics and machine learning. In this post you will discover the linear regression algorithm, how it works and how you can best use it in on your machine learning projects. In this post you will lear...
(:,2),y,' o ');hold onplot(x(:,2),x*theta', '-');hold onplot(3.5,[1,3.5]*theta','x','Color','r')plot(7,[1,7]*theta','x','Color','r')xlabel('Age in years')ylabel('Height in meter s ')legend('Training Data','Linear Regression','Prediction1&2')title('Training...
In Machine Learning, predicting the future is very important. How Does it Work? Python has methods for finding a relationship between data-points and to draw a line of linear regression. We will show you how to use these methods instead of going through the mathematic formula. ...
models have to be considered (such aspolynomial regression,neural networks, orkernel support vector machines). In this chapter, we are going to analyze different situations, showing how to measure the performance of an algorithm and how to make the most appropriate decision to solve specific ...