弱监督机器学习研究新进展（42页）.pdf

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1、July 23,2017 Recent Advances in Machine Learning from Weak Supervision Masashi Sugiyama Director,RIKEN Center for Advanced Intelligence Project(AIP)Professor,The University of Tokyo CCAI2017 Sugiyama,Suzuki&Kanamori,Density Ratio Estimation in Machine Learning,Cambridge University Press,2012 Sugiyam

2、a&Kawanabe,Machine Learning in Non-Stationary Environments,MIT Press,2012 Sugiyama,Statistical Reinforcement Learning,Chapman and Hall/CRC,2015 Supervised learning Reinforcement learning Unsupervised learning Textbooks Sugiyama,Introduction to Statistical Machine Learning,Morgan Kaufmann,2015 Quione

3、ro Sugiyama,Schwaighofer&Lawrence,Dataset Shift in Machine Learning,MIT Press,2009.In Japanese,(Chinese&Korean)What Is My Talk about?Machine learning from big data is successful.Great work on large-scale parallel implementation.However,there are various applications where massive labeled data is not

4、 available.Medicine,manufacturing,disaster,infrastructure In this talk,I will introduce our recent advances in classification from limited information.2 Supervised Classification Binary classification from labeled samples:A large number of labeled samples yield better classification performance.Opti

5、mal convergence rate:3 Positive Negative Decision boundary Unsupervised Classification 4 Since collecting labeled samples is costly,lets learn a classifier from unlabeled data.This is equivalent to clustering.To justify this,need the assumption that each cluster corresponds to each class.This is rar

6、ely satisfied in practice.Semi-Supervised Classification Use a large number of unlabeled samples and a small number of labeled samples:Find a decision boundary along cluster structure induced by unlabeled samples:Sometimes very useful!But same weakness as unsupervised classification.5 Positive Negat