参考:https://codesachin.wordpress.com/2015/08/05/on-the-biasvariance-tradeoff-in-machine-learning/ 之前一直没搞明白什么是bias,什么是variance,现在看看这篇博文. 当你的模型太简单,也就是你的train error太大的时候,你的bias就会比较大:当你的模型变得复杂时,bias变小,同时模型变得比较senstive,variance就会变大 但bias变化的幅度更大,所有整体看来,cros…

bias–variance tradeoff 通过机器学习,我们可以从历史数据学到一个\(f\),使得对新的数据\(x\),可以利用学到的\(f\)得到输出值\(f(x)\).设我们不知道的真实的\(f\)为\(\overline{f}\),我们从数据中学到的\(f\)为\(f^{*}\),实际上\(f^{*}\)是\(\overline{f}\)的一个估计.在统计中,变量\(x\)的均值\(mean\)表示为\(\mu\),方差\(variance\)表示为\(\sigma\),假设我们抽取出…

结论 模型复杂度↑Bias↓Variance↓ 例子 $y_i=f(x_i)+\epsilon_i,E(\epsilon_i)=0,Var(\epsilon_i)=\sigma^2$ 使用knn做预测,在点$x_0$处的Excepted prediction error: $EPE(x_0)=E\left[\left(y_0-\hat{f}(x_0)\right)^2|x_0\right]\\ \ \ =E\left[\left(y_0-E(y_0)\right)^2|x_0\right]+\l…

有监督学习中,预测误差的来源主要有两部分,分别为 bias 与 variance,模型的性能取决于 bias 与 variance 的 tradeoff ,理解 bias 与 variance 有助于我们诊断模型的错误,避免 over-fitting 或者 under-fitting. 原文在这里: https://www.cnblogs.com/ooon/p/5711516.html 博主大概翻译自英文: http://scott.fortmann-roe.com/docs/BiasVaria…

线性回归中有欠拟合与过拟合,例如下图: 则会形成欠拟合, 则会形成过拟合. 尽管五次多项式会精确的预测训练集中的样本点,但在预测训练集中没有的数据,则不能很好的预测,也就是说有较大的泛化误差,上面的右边与左边的图都有很大的泛化误差,他们的情况各不相同,如果数据是非线性的,我们无法使用线性模型来精确的预测,即它的偏差很大,引起欠拟合.而如果像上面右图那样形成一个五次多项式的模型,很可能是我们的训练集数据很小的情况下建立的,它就不能反映出x与y更广泛的关系,这种模型有很大的偏差,引起过拟合.所以归根…

昨天总结了深度学习的资料,今天把机器学习的资料也总结一下(友情提示:有些网站需要"科学上网"^_^) 推荐几本好书: 1.Pattern Recognition and Machine Learning (by Hastie, Tibshirani, and Friedman's ) 2.Elements of Statistical Learning(by Bishop's) 这两本是英文的,但是非常全,第一本需要有一定的数学基础,第可以先看第二本.如果看英文觉得吃力,推荐看一下下面…

<Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机.神经网络.决策树.SVM.Adaboost到随机森林.Deep Learning. <Deep Learning in Neural Networks: An Overview> 介绍:这是瑞士人工智能实验室Jurgen Schmidhuber写的最新版本<神经网络与深度学习综述>本综述的特点是以时间排序,从1940年开始讲起,到60-80…

<Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机.神经网络.决策树.SVM.Adaboost 到随机森林.Deep Learning. <Deep Learning in Neural Networks: An Overview> 介绍:这是瑞士人工智能实验室 Jurgen Schmidhuber 写的最新版本<神经网络与深度学习综述>本综述的特点是以时间排序,从 1940 年开始讲起,到…

转自:机器学习(Machine Learning)&深度学习(Deep Learning)资料 <Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机.神经网络.决策树.SVM.Adaboost到随机森林.Deep Learning. <Deep Learning in Neural Networks: An Overview> 介绍:这是瑞士人工智能实验室Jurgen Schmidhuber写的最…

转载:http://dataunion.org/8463.html?utm_source=tuicool&utm_medium=referral <Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机.神经网络.决策树.SVM.Adaboost到随机森林.Deep Learning. <Deep Learning in Neural Networks: An Overview> 介绍:这是瑞士人工智…

Problems[show] Classification Clustering Regression Anomaly detection Association rules Reinforcement learning Structured prediction Feature engineering Feature learning Online learning Semi-supervised learning Unsupervised learning Learning to rank…

看到Max Welling教授主页上有不少学习notes,收藏一下吧,其最近出版了一本书呢还,还没看过. http://www.ics.uci.edu/~welling/classnotes/classnotes.html Statistical Estimation [ps]- bayesian estimation- maximum a posteriori (MAP) estimation- maximum likelihood (ML) estimation- Bias/Variance…

机器学习(Machine Learning)&深度学习(Deep Learning)资料 機器學習.深度學習方面不錯的資料,轉載. 原作:https://github.com/ty4z2008/Qix/blob/master/dl.md 原作作者會不斷更新.本文更新至2014-12-21 <Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍非常全面.从感知机.神经网络.决策树.SVM.Adaboost到随机森林.Deep L…

##Advice for Applying Machine Learning Applying machine learning in practice is not always straightforward. In this module, we share best practices for applying machine learning in practice, and discuss the best ways to evaluate performance of the le…

<Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机.神经网络.决策树.SVM.Adaboost到随机森林.Deep Learning. <Deep Learning in Neural Networks: An Overview> 介绍:这是瑞士人工智能实验室Jurgen Schmidhuber写的最新版本<神经网络与深度学习综述>本综述的特点是以时间排序,从1940年开始讲起,到60-80…

Why The Golden Age Of Machine Learning is Just Beginning Even though the buzz around neural networks, artificial intelligence, and machine learning has been relatively recent, as many know, there is nothing new about any of these methods. If so many…

源码:https://github.com/cheesezhe/Coursera-Machine-Learning-Exercise/tree/master/ex5 Introduction: In this exercise, you will implement regularized linear regression and use it to study models with different bias-variance properties. 1. Regularized Lin…

当我们运行一个学习算法时,如果这个算法的表现不理想,那么有两种原因导致:要么偏差比较大.要么方差比较大.换句话说,要么是欠拟合.要么是过拟合.那么这两种情况,哪个和偏差有关.哪个和方差有关,或者是不是和两个都有关,搞清楚这点很重要.能判断出现的情况是这两种中的哪一种,是一个很有效的指示器,指引着可以改进算法的最有效的方法和途径. 下面深入地探讨一下有关偏差和方差的问题,并且能弄清楚怎样评价一个学习算法.能够判断一个算法是偏差还是方差有问题.因为这个问题对于弄清如何改进学习算法的效果非常重要. 如…

Machine Learning Methods: Decision trees and forests This post contains our crib notes on the basics of decision trees and forests. We first discuss the construction of individual trees, and then introduce random and boosted forests. We also discuss…

About this Course You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how. Much of this content has never been…

机器学习中遗忘的数学知识 最大似然估计( Maximum likelihood ) 最大似然估计,也称为最大概似估计,是一种统计方法,它用来求一个样本集的相关概率密度函数的参数.这个方法最早是遗传学家以及统计学家罗纳德·费雪爵士在1912年至1922年间开始使用的. 最大似然估计的原理 给定一个概率分布,假定其概率密度函数(连续分布)或概率质量函数(离散分布)为,以及一个分布参数,我们可以从这个分布中抽出一个具有个值的采样,通过利用,我们就能计算出其概率: 但是,我们可能不知道的值,尽管我们知道…

1    Unsupervised Learning 1.1    k-means clustering algorithm 1.1.1    算法思想 1.1.2    k-means的不足之处 1.1.3    如何选择K值 1.1.4    Spark MLlib 实现 k-means 算法 1.2    Mixture of Gaussians and the EM algorithm 1.3    The EM Algorithm 1.4    Principal Components…

Machine Learning Algorithms Study Notes 高雪松 @雪松Cedro Microsoft MVP 目 录 1    Introduction    1 1.1    What is Machine Learning    1 1.2    学习心得和笔记的框架    1 2    Supervised Learning    3 2.1    Perceptron Learning Algorithm (PLA)    3 2.1.1    PLA -- "知…

In Week 6, you will be learning about systematically improving your learning algorithm. The videos for this week will teach you how to tell when a learning algorithm is doing poorly, and describe the 'best practices' for how to 'debug' your learning…

Logistic regression is a method for classifying data into discrete outcomes. For example, we might use logistic regression to classify an email as spam or not spam. In this module, we introduce the notion of classification, the cost function for logi…

Chapter 1 Introduction 1.1 What Is Machine Learning? To solve a problem on a computer, we need an algorithm. An algorithm is a sequence of instructions that should be carried out to transform the input to output. For example, one can devise an algori…

Machine Learning Done Wrong Statistical modeling is a lot like engineering. In engineering, there are various ways to build a key-value storage, and each design makes a different set of assumptions about the usage pattern. In statistical modeling, th…

In machine learning, is more data always better than better algorithms? No. There are times when more data helps, there are times when it doesn't. Probably one of the most famous quotes defending the power of data is that of Google's Research Directo…

机器学习系统设计(Building Machine Learning Systems with Python)- Willi Richert Luis Pedro Coelho 总述 本书是 2014 的,看完以后才发现有第二版的更新,2016.建议阅读最新版,有能力的建议阅读英文版,中文翻译有些地方比较别扭(但英文版的书确实是有些贵). 我读书的目的:泛读主要是想窥视他人思考的方式. 作者写书的目标:面向初学者,但有时间看看也不错.作者说"我希望它能激发你的好奇心,并足以让你保持渴望,不断探索…

Week1: Machine Learning: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. Supervised Learning:We alr…