Learning from data 练习3.2
导言 本篇博文介绍Learning from data一书中Exercise3.2的答案 题目 题意梗概: 采样100个训练样本，1000个测试样本。随机选一条直线作为target function生成label。将中间十分之一的样本的标签反转，来构筑一个不线性可分的样本。用pocket版本的PLA算法进行分类，记录训练过程中in-sample error和out-of-sample error的
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相关文章
- 1. Learning from Uncertainty for Big Data
- 2. Communication-Efficient Learning of Deep Networks from Decentralized Data
- 3. 研究生课程——learning from data——lecture1:introduction
- 4. [NAS-AutoAug]AutoAugment: Learning Augmentation Strategies from Data
- 5. 《AutoAugment: Learning Augmentation Strategies from Data》笔记
- 6. Reinforcement Learning for Relation Classification from Noisy Data
- 7. Learning From Data 第一章总结
- 8. 论文阅读：AutoAugment: Learning Augmentation Strategies from Data
- 9. Learning from Semi-Supervised Weak-Label Data理解
- 10. 【Learning from data】Lecture 2_Learning to answer yes or no