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# System prepended metadata

title: 機器學習基石下 - 延伸閱讀
tags: [機器學習基石下：方法篇]

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tags: 機器學習基石下：方法篇
---

機器學習基石下 - 延伸閱讀
===

## Content

[TOC]

## [Slides & Videos](https://www.csie.ntu.edu.tw/~htlin/mooc/)


## 預備知識

機器學習基石上及其作業零 (機率統計、線性代數、微分之基本知識)

## 參考書籍

Learning from Data: A Short Course , Abu-Mostafa, Magdon-Ismail, Lin, 2013.

## 參考文獻

G. Sever, A. Lee. Linear Regression Analysis, 2nd Edition, Wiley, 2003. (第九講：Linear Regression 由統計學的角度來分析；第十二到十三講：Polynomial Transform 後再做 Linear Regression)

D. C. Hoaglin, R. E. Welsch. The hat matrix in regression and ANOVA. American Statistician, 32:17–22, 1978. (第九講：Linear Regression 的 Hat Matrix)

D. W. Hosmer, Jr., S. Lemeshow, R. X. Sturdivant. Applied Logistic Regression, 3rd Edition, Wiley, 2013 (第十講：Logistic Regression 由統計學的角度來分析)

T. Zhang. Solving large scale linear prediction problems using stochastic gradient descent algorithms. International Conference on Machine Learning, (第十一講：Stochastic Gradient Descent 用在線性模型的理論分析)

R. Rifkin, A. Klautau. In Defense of One-Vs-All Classification. Journal of Machine Learning Research, 5: 101-141, 2004. (第十一講：One-versus-all)

J. Fürnkranz. Round Robin Classification. Journal of Machine Learning Research, 2: 721-747, 2002. (第十一講：One-versus-one)

L. Li, H.-T. Lin. Optimizing 0/1 loss for perceptrons by random coordinate descent. In Proceedings of the 2007 International Joint Conference on Neural Networks (IJCNN ’07), pages 749–754, 2007. (第十一講：一個由最佳化角度出發的 Perceptron Algorithm)

G.-X. Yuan, C.-H. Ho, C.-J. Lin. Recent advances of large-scale linear classification. Proceedings of IEEE, 2012. (第十一講：更先進的線性分類方法)

Y.-W. Chang, C.-J. Hsieh, K.-W. Chang, M. Ringgaard, C.-J. Lin. Training and testing low-degree polynomial data mappings via linear SVM. Journal of Machine Learning Research, 11(2010), 1471-1490. (第十二講：一個使用多項式轉換加上線性分類模型的方法)

M. Magdon-Ismail, A. Nicholson, Y. S. Abu-Mostafa. Learning in the presence of noise. In Intelligent Signal Processing. IEEE Press, 2001. (第十三講：Noise 和 Learning)

A. Neumaier, Solving ill-conditioned and singular linear systems: A tutorial on regularization, SIAM Review 40 (1998), 636-666. (第十四講：Regularization)

T. Poggio, S. Smale. The mathematics of learning: Dealing with data. Notices of the American Mathematical Society, 50(5):537–544, 2003. (第十四講：Regularization)

P. Burman. A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods. Biometrika, 76(3): 503–514, 1989. (第十五講：Cross Validation)

R. Kohavi. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th International Joint Conference on Artificial intelligence (IJCAI ’95), volume 2, 1137–1143, 1995. (第十五講：Cross Validation)

A. Blumer, A. Ehrenfeucht, D. Haussler, and M. K. Warmuth. Occam’s razor. Information Processing Letters, 24(6):377–380, 1987. (第十六講：Occam's Razor)





