Dates: 35, December 2014
Place： 3rd floor, Seminar room, Ikenohata Building, RIKEN BSI (理化学研究所和光キャンパス，脳科学総合研究センター池之端棟、３階セミナー室)
Registration: Registration is required.
Aim and Scope:
Information geometry started its history in 1980's. This framework has its mathematical background in Riemannian geometry and affine geometry, and has been applied to many fields including statistics, control theory, signal processing, optimization theory, and information theory. Information geometry gave better understanding of the problems in those fields and provided some clues for improvements.
Machine learning is a collection of theories and methodologies developed in order to deal with new types of data. Each theory and methodology has its background in a related field, such as, statistics, computer science and optimization theory, where information geometry have made a lot of contributions. For further improvements of machine learning methods, information geometrical viewpoint will be useful. In this workshop, recent results of information geometry and machine learning will be reviewed, and future direction will be discussed.
Confirmed speakers:
(All speakers are invited. The speaker list has been changed on 15 Oct 2014.)
Organizers:
Contact:
