Centroid Estimation With Guaranteed Efficiency: A General Framework for Weakly Supervised Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence(2022)
摘要
In this paper, we propose a general framework termed centroid estimation with guaranteed efficiency (CEGE) for weakly supervised learning (WSL) with incomplete, inexact, and inaccurate supervision. The core of our framework is to devise an unbiased and statistically efficient risk estimator that is applicable to various weak supervision. Specifically, by decomposing the loss function (e.g., the sq...
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关键词
Estimation,Supervised learning,Fasteners,Training data,Support vector machines,Semisupervised learning,Safety
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