Machine Learning --- Structure risk & VC dimension 一、结构风险 结构风险=经验风险+置信风险 经验风险=分类器的训练误差 置信风险=分类器的测试误差 其中置信风险由样本数量N与分类函数的VC维h决定。样本数量越多模型越接近真实分布,置信风险越小;VC维越大,模型越复杂推广性差,置信风险越大。结构风险公式如下:...
Foundations of Machine Learning: Rademacher complexity and VC-Dimension(2)(一) 增长函数(Growth function) 在引入增长函数之前,我们先介绍一个例子,这个例子会有助于理解增长函数这个东西。在input spac
摘要:本文主要向大家介绍了VC编程之Foundataions of Machine Learning: Rademacher complexity and VC-Dimension,通过具体的内容向大家展示,希望对大家学习VC编程有所帮助。 本文主要向大家介绍了VC编程之Foundataions of Machine Learning: Rademacher complexity and VC-Dimension,通过具体的内容向大家展示,希望对大家学...
关键词: Theoretical or Mathematical/ learning by example learning systems neural nets pattern recognition/ VC-dimension learning machine capacity measurement error rates data sets linear classifiers/ C1230D Neural nets C1240 Adaptive system theory C1250 Pattern recognition ...
8.2VC-Dimension 由上我们可以看到,尽管有限假设类是PAC Learnable的充分条件,但是它不是必要条件,接下来我们将展示VC Dimension是如何给出可学习性的充要条件的 No-Free-Lunch告诉我们,如果我们不给一个假设类设置任何限制,我们总能找到一个分布使得学习算法大概率会得到一个差的结果,尽管假设类中也存在一个函数能够...
4. lower bounds Several upper bounds on the generalization error are presented above. Some lower bounds can also be provided in terms of the VC-dimension, which imply that with an infinite VC-dimension, realizable and agnostic PAC-learning is not possible. 编辑...
Machine Learning: 10701 and 15781, 2003 Assignment 4 Primary contact for questions and clarifications: Rong Zhang (rongz@cs.cmu.edu) Due Date: 10.30am Tuesday November 4th (one week) Group Work: You may work in a group of two people if you wish. 1. VC Dimension (30 Points) ...
VC Demision_Andrew Moore
The technical term for “labeling in all possible ways” is to “shatter” the input space. The size of that input space is called the VC dimension, where VC stands for Vapnik-Chervonenkis. In this example, size of the largest set of inputs this hypothesis class can shatter is 1 element...
Anexampleofoverfitting Whatifthetrainingsetinfinite? 7 Outline Overfitting ◦True,training,testingerrors,andoverfitting PAClearning(finitehypothesisspace) ◦Consistentlearnercase,andagnosticcase PAClearning(infinitehypothesisspace) ◦VCdimension,VCbounds,structuralrisk ...