.png){kind=icon}
返回
Information theoretic perspective on sample complexity
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neunet.2023.08.032.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The statistical supervised learning framework assumes an input-output set with a joint probability distribution that is reliably represented by the training dataset. The learning system is then required to output a prediction rule learned from the training dataset's input-output pairs. In this work, we investigate the relationship between the sample complexity, the empirical risk and the generalization error based on the asymptotic equipartition property (AEP) (Shannon, 1948). We provide theoretical guarantees for reliable learning under the information-theoretic AEP, with respect to the generalization error and the sample size in different settings.(c) 2023 Elsevier Ltd. All rights reserved.
Keyword:
Information theory
Supervised learning
Sample complexity
Generalization
期刊
IF:
6.3
论文数:
7.9K
被引数:
3.0W
机构
引用论文
Improved Particle Size Control for the Dispersion Polymerization of Methyl methacrylate in Supercritical Carbon Dioxide超临界二氧化碳中甲基丙烯酸甲酯分散聚合的改进粒度控制
OAAI
OAAI分享收藏A concise synthesis of (−)-conduritol F from l-quebrachitol via AlCl3-n-Bu4NI mediated demethylation
PREAI
分享收藏
Responses of the Roanoke Bayhead Delta to variations in sea level rise and sediment supply during the Holocene and Anthropocene.png)
Anthropocene
AnthropoceneIF0
PREAI
分享收藏The complete genome sequence of the Gram-positive bacterium Bacillus subtilis革兰氏阳性细菌枯草芽孢杆菌的全基因组序列NatureOAAI
NatureIF0
OAAI分享收藏 没有更多内容
没有更多内容