Conclusions and Future Prospects
Masashi Sugiyama, Motoaki Kawanabe · The MIT Press eBooks · 2012
This chapter summarizes the main themes covered in the preceding discussions and discusses future prospects. This book has provided a comprehensive overview of theory, algorithms, and applications of machine learning under covariate shift. Beyond covariate shift adaptation, it has been shown recently that the ratio of probability densities can be used for solving machine learning tasks. This novel machine learning framework includes multitask learning, privacy-preserving data mining, outlier detection, change detection in time series, two-sample test, conditional density estimation, and probabilistic classification. Furthermore, mutual information—which plays a central role in information theory—can be estimated via density ratio estimation.