BriefGPT.xyz
Jun, 2012
弱监督分类器的凸松弛
A Convex Relaxation for Weakly Supervised Classifiers
HTML
PDF
Armand Joulin, Francis Bach
TL;DR
该论文提出了一种通用的多类弱监督分类方法,其中引入了一种基于凸松弛的代价函数来解决软最大损失的本地极小问题,于是设计了一种算法来高效地解决相应的半定规划问题,并在不同数据集上表现出较好的效果,包括多实例学习和半监督学习,以及聚类任务。
Abstract
This paper introduces a general
multi-class approach
to
weakly supervised classification
. Inferring the labels and learning the parameters of the model is usually done jointly through a block-coordinate descent a
→