R3 on "W.a.t.m.i. statistical ideas of the past 50 years? " Andrew Gelman, Aki Vehtari(28)

R3(References on References on References) on "What are the most important statistical ideas of the past 50 years? " Andrew Gelman, Aki Vehtari(28)

R3 on "What are the most important statistical ideas of the past 50 years? " Andrew Gelman, Aki Vehtari(0)
https://qiita.com/kaizen_nagoya/items/a8eac9afbf16d2188901


What are the most important statistical ideas of the past 50 years?
Andrew Gelman, Aki Vehtari
https://arxiv.org/abs/2012.00174

# References 
# 28
Cortes, C., and Vapnik, V. (1995). Support-vector networks. Machine Learning 20, 273–297.

https://link.springer.com/article/10.1007/BF00994018

## Reference on 28

## 28.1

Aizerman, M., Braverman, E., & Rozonoer, L. (1964). Theoretical foundations of the potential function method in pattern recognition learning.Automation and Remote Control, 25:821–837.




## 28.2
Anderson, T.W., & Bahadur, R.R. (1966). Classification into two multivariate normal distributions with different covariance matrices.Ann. Math. Stat., 33:420–431.

https://projecteuclid.org/journalArticle/Download?urlId=10.1214%2Faoms%2F1177704568&referring

### Reference on 28.2

### 28.2.1

 Regression Models and Life-Tables
D. R. Cox
Journal of the Royal Statistical Society. Series B (Methodological), Vol. 34, No. 2. (1972), pp. 187-220.

REFERENCES 

[1] ANDERSON, T. W. (1958). An Introduction to Multivariate Statistical Analysis. Wiley, New York: ]2] CaveLLI, L. L. (1945). Alcuni problemi della ans1isi biometrica di popolasioni natursli. Mem. Isl. Ital. Idrobiol. 2 301-323. CLIINIES-ROSS, C. W. and IFtunnnarpon, R. 11. (1960). Geometry and linear discrim-ination. Biometrika 47 185-189. [4] KELLBACE, SOLOMON (1959). Information Theory and Statistics. Wiley, New York. [5] PENROSE, L. S. (1947). Some notes on discrimination. Ann. Eugenics 13 228-237. 16] SMITH, C. A. B. (1947). Some examples of discrimination. Ann. Eugenics 13 272-282. RI WELCH, PETER and Wsnrnsan, RICHARD S. (1961). Two multivariate statistical com-puter programs and their application to the vowel recognition problem. J. Acoustical Soc. of America 33 426-434. 


## 28.3
Boser, B.E., Guyon, I., & Vapnik, V.N. (1992). A training algorithm for optimal margin classifiers. InProceedings of the Fifth Annual Workshop of Computational Learning Theory, 5, 144–152. Pittsburgh, ACM.

## 28.4 
Bottou, L., Cortes, C., Denker, J.S., Drucker, H., Guyon, I., Jackel, L.D., LeCun, Y., Sackinger, E., Simard, P., Vapnik, V., & Miller, U.A. (1994). Comparison of classifier methods: A case study in handwritten digit recognition.Proceedings of 12th International Conference on Pattern Recognition and Neural Network.
Bromley, J., & Sackinger, E. (1991). Neural-network andk-nearest-neighbor classifiers. Technical Report 11359-910819-16TM, AT&T.
Cournant, R., & Hilbert, D. (1953).Methods of Mathematical Physics, Interscience, New York.
## 28.5
 
Fisher, R.A. (1936). The use of multiple measurements in taxonomic problems.Ann. Eugenics, 7:111–132.

## 28.6
LeCun, Y. (1985). Une procedure d'apprentissage pour reseau a seuil assymetrique.Cognitiva 85: A la Frontiere de l'Intelligence Artificielle des Sciences de la Connaissance des Neurosciences, 599–604, Paris.
LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., & Jackel, L.D. (1990). Handwritten digit recognition with a back-propagation network.Advances in Neural Information Processing Systems, 2, 396–404, Morgan Kaufman.

## 28.7 
Parker, D.B. (1985). Learning logic. Technical Report TR-47, Center for Computational Research in Economics and Management Science, Massachusetts Institute of Technology, Cambridge, MA.
Rosenblatt, F. (1962).Principles of Neurodynamics, Spartan Books, New York.

## 28.8 
Rumelhart, D.E., Hinton, G.E., & Williams, R.J. (1986). Learning internal representations by backpropagating errors.Nature, 323:533–536.

## 28.9 
Rumelhart, D.E., Hinton, G.E., & Williams, R.J. (1987). Learning internal representations by error propagation. In James L. McClelland & David E. Rumelhart (Eds.),Parallel Distributed Processing, 1, 318–362, MIT Press.

## 28.10
Vapnik, V.N. (1982).Estimation of Dependences Based on Empirical Data, Addendum 1, New York: Springer-Verlag.

# 参考資料(References)
Data Scientist の基礎(2)
https://qiita.com/kaizen_nagoya/items/8b2f27353a9980bf445c

岩波数学辞典　二つの版がCDに入ってお得
https://qiita.com/kaizen_nagoya/items/1210940fe2121423d777

岩波数学辞典
https://qiita.com/kaizen_nagoya/items/b37bfd303658cb5ee11e

アンの部屋（人名から学ぶ数学：岩波数学辞典）英語(24)
https://qiita.com/kaizen_nagoya/items/e02cbe23b96d5fb96aa1

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