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前几题都是抄的原题,后面几题太长了,就写了个大概。
1, 请说明参数正则化和参数先验之间的联系;并解释在机器学习模型参数估计中用正则化的目的是什么。(6分)
2. 请给出条件熵的定义。并举一本课程应用该方法的例子(应该是一本课程,不是一本例子)。说明这样做的好处是什么。给出你的直观解释。(8分)
3.
朴素贝叶斯模型的基本假设是什么?这样的假设会带来什么样的好处。如果在实际应用中该假设满足,则朴素贝叶斯是出错概率意义下的最优分类器吗?请说明为什么?(8分)
(这题没有原题目了,只有我记的草稿了,题目太长了)
4. K-means和GMM在EM算法中的相同点和不同点。K-means和GMM的优势和劣势。GMM中E步更新的公式和M步最大化的期望公式。(8分)
5.
线性可分的SVM的目标函数与约束条件,并给出解的形式(数学公式)。在SVM中,可以对目标和约束直接优化求解,但我们使用了对偶优化,这样做的好处是什么。(8分)
6. EM算法中优化GMM时会出现协方差矩阵不可逆的情况。这种情况出现的原因一般是什么?如果出现的话,你是如何解决的。(6分)
(两个GMM?)
7. 逻辑回归的分离器是否是线性分离器。如果是,证明;如果不是,说明为什么。
8. 向量(函数)内积在机器学习课程中使用的三个例子。并描述出现的形式,给出自己的理解。(8分)
19届计算机学长就这样了,听说20届超级卷,加油吧。不过这个卷子竟有19年的原题,说不定明年就是2020的?不过PCA不考属实不明白了,而且考的很刁钻,我还看了很久的PCA。退钱!!!