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Bias and Variance

Bias and Variance of Estimator. Variance depends on the number of samples.
2018-04-16 上传大小:1.12MB
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Neural Networks and the Bias/Variance Dilemma

Stuart Geman在1992年的论文,周志华的机器学习中也提到过,关于偏差 泛化误差的计算也源自这篇论文。英文的。

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Machine_Learning_Yearning_V03

Here are more chapters of Machine Learning Yearning. These, along with next week’s chapters, teach you how to use Bias and Variance to prioritize improvements to your ML project. Older ideas about Bias/Variance, for example the “Bias/Variance tradeoff”, are becoming less useful in the deep learning era, and modern ML needs updated guidelines. Read this week’s chapters to learn more!

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BiasVarianceTradeoff

详细讲解了Bias和Variance直接的关系,学习机器学习的不错资料

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Machine Learning Yearning(吴恩达老师最新章节)

Once you have identified whether your algorithm has high bias or variance, these chapters discuss specific techniques to address the two.

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斯坦福机器学习编程作业machine-learning-ex5,方差与误差, Bias v.s. Variance题目,满分,2015最新作业答案

斯坦福机器学习编程作业machine-learning-ex5,方差与误差, Bias v.s. Variance题目,满分,2015最新作业答案 MATLAB代码

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机器学习记忆卡片(Learning from data 01)

个人做的Learning from data 记忆卡片。涉及VC维度,variance, bias,霍夫丁不等式等topic。建议配合书看完Chapter1和Chapter2(重点)使用。

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实操机器学习指南:Machine Learning Yearning -- by 吴恩达

深度学习和机器学习研发中的技巧和注意事项: 1 Why Machine Learning Strategy 2 How to use this book to help your team 3 Prerequisites and Notation 4 Scale drives machine learning progress 5 Your development and test sets 6 Your dev and test sets should come from the same distribution 7 How large do the dev/test sets need to be? 8 Establish a single-number evaluation metric for your team to optimize 9 Optimizing and satisfying metrics 10 Having a dev set and metric speeds up iterations 11 When to change dev/test sets and metrics 12 Takeaways: Setting up development and test sets 13 Build your first system quickly, then iterate 14 Error analysis: Look at dev set examples to evaluate ideas 15 Evaluating multiple ideas in parallel during error analysis 16 Cleaning up mislabeled dev and test set examples 17 If you have a large dev set, split it into two subsets, only one of which you look at 18 How big should the Eyeball and Blackbox dev sets be 19 Takeaways: Basic error analysis 20 Bias and Variance: The two big sources of error 21 Examples of Bias and Variance 22 Comparing to the optimal error rate 23 Addressing Bias and Variance 24 Bias vs. Variance tradeoff 25 Techniques for reducing avoidable bias 26 Techniques for reducing Variance 27 Error analysis on the training set 28 Diagnosing bias and variance: Learning curves 29 Plotting training error 30 Interpreting learning curves: High bias 31 Interpreting learning curves: Other cases 32 Plotting learning curves 33 Why we compare to human-level performance 34 How to define human-level performance 35 Surpassing human-level performance 36 Why train and test on different distributions 37 Whether to use all your data 38 Whether to include inconsistent data 39 Weighting data 40 Generalizing from the training set to the dev set 41 Addressing Bias and Variance 42 Addressing data mismatch 43 Artificial data synthesis 44 The Optimization Verification test 45 General form of Optimization Verification test 46 Reinforcement learning example 47 The rise of end-to-end learning 48 More end-to-end learning examples 49 Pros and cons of end-to-end learning 50 Learned sub-components 51 Directly learning rich outputs 52 Error Analysis by Parts 53 Beyond supervised learning: What’s next? 54 Building a superhero team - Get your teammates to read this 55 Big picture 56 Credits

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Machine_Learning_Yearning_V01

Table of Contents (draft) Why Machine Learning Strategy 4 ........................................................................................... How to use this book to help your team 6 ................................................................................ Prerequisites and Notation 7 .................................................................................................... Scale drives machine learning progress 8 ................................................................................ Your development and test sets 11 ............................................................................................ Your dev and test sets should come from the same distribution 13 ........................................ How large do the dev/test sets need to be? 15 .......................................................................... Establish a single-number evaluation metric for your team to optimize 16 ........................... Optimizing and satisficing metrics 18 ..................................................................................... Having a dev set and metric speeds up iterations 20 ............................................................... When to change dev/test sets and metrics 21 .......................................................................... Takeaways: Setting up development and test sets 23 .............................................................. Build your first system quickly, then iterate 25 ........................................................................ Error analysis: Look at dev set examples to evaluate ideas 26 ................................................ Evaluate multiple ideas in parallel during error analysis 28 ................................................... If you have a large dev set, split it into two subsets, only one of which you look at 30 ........... How big should the Eyeball and Blackbox dev sets be? 32 ...................................................... Takeaways: Basic error analysis 34 .......................................................................................... Bias and Variance: The two big sources of error 36 ................................................................. Examples of Bias and Variance 38 ............................................................................................ Comparing to the optimal error rate 39 ................................................................................... Addressing Bias and Variance 41 .............................................................................................. Bias vs. Variance tradeoff 42 ..................................................................................................... Techniques for reducing avoidable bias 43 .............................................................................. Techniques for reducing Variance 44 ....................................................................................... Error analysis on the training set 46 ........................................................................................ Diagnosing bias and variance: Learning curves 48 ................................................................. Plotting training error 50 .......................................................................................................... Interpreting learning curves: High bias 51 ............................................................................... Interpreting learning curves: Other cases 53 .......................................................................... Plotting learning curves 55 ....................................................................................................... Why we compare to human-level performance 58 .................................................................. How to define human-level performance 60 ........................................................................... Surpassing human-level performance 61 ................................................................................ Why train and test on different distributions 63 ...................................................................... Page!2 Machine Learning Yearning-Draft V0.5 Andrew NgWhether to use all your data 65 ................................................................................................ Whether to include inconsistent data 67 .................................................................................. Weighting data 68 .................................................................................................................... Generalizing from the training set to the dev set 69 ................................................................ Addressing Bias and Variance 71 ............................................................................................. Addressing data mismatch 72 ................................................................................................... Artificial data synthesis 73 ........................................................................................................ The Optimization Verification test 76 ...................................................................................... General form of Optimization Verification test 78 ................................................................... Reinforcement learning example 79 ......................................................................................... The rise of end-to-end learning 82 ........................................................................................... More end-to-end learning examples 84 .................................................................................. Pros and cons of end-to-end learning 86 ................................................................................ Learned sub-components 88 .................................................................................................... Directly learning rich outputs 89 .............................................................................................. Error Analysis by Parts 93 ....................................................................................................... Beyond supervised learning: What’s next? 94 ......................................................................... Building a superhero team - Get your teammates to read this 96 ........................................... Big picture 98 ............................................................................................................................ Credits 99

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MT4上用的指标BIAS-乖离率指标,原始代码编写,好用

MT4上用的指标BIAS-乖离率指标,原始代码编写,好用

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JESD22-A108

Temperature, Bias, and Operating Life

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allan variance

对原始的钟差数据处理,将其转化为频率数据,通过频率数据求阿伦方差,来分别求解卫星钟的千秒稳,万秒稳,天稳,从而对卫星钟性能进行分析。

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Bias_Circuits_for_RF_Devices

bias circuit for RF devices.

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Variance Shadow Maps

Variance Shadow Maps

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hadamard variance

采用哈达吗方差,相当于对相位数据进行三次差分处理,将相位数据转化为频率数据,求得卫星钟的,天稳,千秒稳,和万秒稳,进而对卫星钟的性能进行分析。

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JESD22-A101C [Steady-State Temperature Humidity Bias Life Test]

JESD22-A101C [Steady-State Temperature Humidity Bias Life Test]

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Bivariate Shrinkage

Bivariate Shrinkage With Local Variance Estimation

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PSpice直流bias传输特性

PSpice仿真直流bias传输特性....

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Variance reduction techniques and quasi-Monte Carlo methods ☆

Variance reduction techniques and quasi-Monte Carlo methods ☆

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Introduction to Variance Estimation - 2nd Edition - 2007

Introduction to Variance Estimation - 2nd Edition - 2007  

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Variance Swaps primer

Variance Swap很好的研究报告

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