Report for: Algorithmic Learning in a Random World

Title	Algorithmic Learning in a Random World
Published by	Springer International Publishing, January 2022
DOI	10.1007/978-3-031-06649-8
ISBNs	978-3-03-106648-1, 978-3-03-106649-8
Authors	Vladimir Vovk, Alexander Gammerman, Glenn Shafer, Vovk, Vladimir, Gammerman, Alexander, Shafer, Glenn

Timeline

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.

Country	Count	As %
United Kingdom	1	33%
Unknown	2	67%

Type	Count	As %
Members of the public	2	67%
Scientists	1	33%

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Country	Count	As %
Unknown	33	100%

Readers by professional status	Count	As %
Student > Ph. D. Student	8	24%
Student > Doctoral Student	2	6%
Other	2	6%
Researcher	2	6%
Student > Postgraduate	2	6%
Other	0	0%
Unknown	17	52%

Readers by discipline	Count	As %
Computer Science	5	15%
Engineering	4	12%
Business, Management and Accounting	2	6%
Mathematics	2	6%
Medicine and Dentistry	1	3%
Other	1	3%
Unknown	18	55%

Algorithmic Learning in a Random World