3.梯度下降 梯度下降法则是一种最优化算法,它是用迭代的方法求解目标函数得到最优解,是在cost function(成本函数)的基础上,利用梯度迭代求出局部最优解。 梯度下降法是按下面的流程进行的: 1)首先对θ赋值,这个值可以是随机的,也可以让θ是一个全零的向量。 2)改变θ的值,使得J(θ)按梯度下降的方向进行减少...
2.平方损失函数(quadratic loss function) L ( Y , f ( X ) ) = ( Y − f ( x ) ) 2 L(Y,f(X)) = (Y – f(x))^2 L(Y,f(X))=(Y−f(x))2 3.绝对值损失函数(absolute loss function) L ( Y , f ( x ) ) = ∣ Y − f ( X ) ∣ L(Y,f(x)) = |Y – f...
PS: 此处平方差公式原本应当是除以 m,这里除以 2m 只为了以后在数学上计算方便,这里除以 m 和 除以 2m 对获得最小的代价函数值没有影响。 $J(\theta_0, \theta_1)$被成为代价函数(cost function),这是回归问题中最常使用的方法. 现在要做的就是得到使 $J(\theta_0, \theta_1)$ 最小的 $\theta_0...
In this video (article), we'll define something called the cost function. This will let us figure out how to fit the best possible straight line to our data. In linear regression we have a training set like that shown here. Remember our notation M was the number of training examples, s...
1% Compute Costforlinear regression2%cost Function函数实现___利用矩阵操作进行!!3function J =computeCost(X, y, theta)45%Initialize some useful values6m = length(y); %number of training examples7J =0;89%Instructions: Compute the cost of a particular choice of theta10% You shouldsetJ to the...
training phase in the form of a single real number is known as “Loss Function”. These are used in those supervised learning algorithms that use optimization techniques. Notable examples of such algorithms are regression,logistic regression, etc. The terms cost function & loss function are ...
1% Compute Costforlinear regression2%cost Function函数实现___利用矩阵操作进行!!3function J =computeCost(X, y, theta)45%Initialize some useful values6m = length(y); %number of training examples7J =0;89%Instructions: Compute the cost of a particular choice of theta10% You shouldsetJ to the...
multiple linear regressionoutliersConsider a times series in simple linear regression. It is shown that under suitable conditions point estimates or predictions for the next time period into the future are unaffected by values of the dependent variable at some given time period in the past. The ...
There is a problem somewhere in your data manipulation. the cost function is right. I started from the beginning for the gradients, now it works: 테마복사 Z2 = X * theta; A2 = sigmoid(Z2); D = (A2 - y); grad(1) = D' * X(:,1); ...
We modify least angle regression algorithms commonly used for sparse linear regression to produce the ParLiR algorithm, whichnot only provides an efficient and parsimonious solution as we demonstrate empirically, but it also provides formal guarantees that we prove theoretically. 展开 关键词:...