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Professor Gammerman studied in Leningrad (now St Petersburg), was awarded his PhD in 1974, and then worked in several Research Institutes of the Academy of Science of the USSR. In 1983 he moved to Great Britain and worked first as a Lecturer and then as a Reader at Heriot-Watt University, Edinburgh. He was appointed to the established Chair in Computer Science at University of London ( Royal Holloway and Bedford New College) in 1993. He served as Head of the Computer Science Department from 1995 to 2005. He is also the Director of the Computer Learning Research Centre at Royal Holloway, University of London.
Professor Gammerman is a Fellow of the Royal Society of Arts, a Fellow of the Royal Statistical Society, a Chartered Fellow of the British Computer Society, the American Association for Artificial Intelligence (AAAI) . He chaired and participated in organising committees of many international conferences and workshops on Machine Learning and Bayesian methods in Europe, Russia and in the United States. He is also a member of the editorial boards of the Law, Probability and Risk journal and The Computer Journal.
Professor Gammerman's current research interest lies in field of Algorithmic Randomness Theory and its applications to machine learning, the development of inductive/transductive confidence machines and intelligent data analysis. Areas in which these techniques have been applied include medical diagnosis, forensic science, genomics, environment and finance. Professor Gammerman has published over a hundred research papers and several books on computational learning and probabalistic reasoning.
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论文共 90 篇作者统计合作学者相似作者
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COPApp.367-368, (2023)
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Algorithmic Learning in a Random Worldpp.145-155, (2022)
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Algorithmic Learning in a Random Worldpp.391-422, (2022)
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Algorithmic Learning in a Random Worldpp.227-263, (2022)
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Algorithmic Learning in a Random Worldpp.265-304, (2022)
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Algorithmic Learning in a Random Worldpp.157-179, (2022)
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Algorithmic Learning in a Random Worldpp.107-142, (2022)
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Algorithmic Learning in a Random Worldpp.363-389, (2022)
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Algorithmic Learning in a Random Worldpp.71-106, (2022)
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