方法证据记录
Akaike Information Criterion
The Akaike Information Criterion is an information-theoretic measure for model selection that balances goodness of fit against model complexity. Introduced by Hirotugu Akaike in 1974, AIC estimates the relative quality of models for a given dataset, penalizing additional parameters to prevent overfitting.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Akaike Information Criterion
分类方法记录 · mcdm / model-evaluation
- Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723. · DOI 10.1109/TAC.1974.1100705
- Burnham, K. P., & Anderson, D. R. (2002). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (2nd ed.). New York: Springer. · DOI 10.2307/3802723
- Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. Annals of Mathematical Statistics, 22(1), 79-86. · DOI 10.1214/aoms/1177729694
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