机器学习7-SVM
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http://pan.baidu.com/s/1bnyCFIB
Question 1
Suppose you have trained an SVM classifier with a Gaussian kernel, and it learned the following decision boundary on the training set:
You suspect that the SVM is underfitting (==high bais)your dataset. Should you try increasing or decreasingC ? Increasing or decreasing σ2 ?
You suspect that the SVM is underfitting (==high bais)your dataset. Should you try increasing or decreasing
Question 2
The formula for the Gaussian kernel is given bysimilarity(x,l(1))=exp(−||x−l(1)||22σ2) . The figure below shows a plot of f1=similarity(x,l(1)) when σ2=1 .
Which of the following is a plot off1 when σ2=0.25 ?
Which of the following is a plot of
Question 3
The SVM solves minθ C∑mi=1y(i)cost1(θTx(i))+(1−y(i))cost0(θTx(i))+∑nj=1θ2j where the functions cost0(z) and cost1(z) look like this:
The first term in the objective is:C∑mi=1y(i)cost1(θTx(i))+(1−y(i))cost0(θTx(i)). This first term will be zero if two of the following four conditions hold true. Which are the two conditions that would guarantee that this term equals zero?
The first term in the objective is:
Question 4
Suppose you have a dataset with n = 10 features and m = 5000 examples. After training your logistic regression classifier with gradient descent, you find that it hasunderfit the training set and does not achieve the desired performance on the training or cross validation sets. Which of the following might be promising steps to take? Check all that apply.
总结: 当feature较多,训练集合较大,出现欠拟合,使用神经网络和带有高斯核的支持向量机
Question 5
Which of the following statements are true? Check all that apply.
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