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个人简介
Professor Gammerman's current research interest lies in machine learning. In particular, the development of conformal predictors -- a set of novel machine learning techniques that guarantee the validity of prediction. The validity here means that a probability of error is equal to or not exceeding a given confidence level. Areas in which these techniques have been applied include medical diagnosis, drug design, forensic science, proteomics, genomics, environment and information security. He has published about two hundred research papers and several books on computational learning and probabilistic inference.
研究兴趣
论文共 145 篇作者统计合作学者相似作者
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CONFORMAL AND PROBABILISTIC PREDICTION WITH APPLICATIONS, VOL 204 (2023): 367-368
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Algorithmic Learning in a Random Worldpp.19-69, (2022)
Algorithmic Learning in a Random Worldpp.145-155, (2022)
Algorithmic Learning in a Random Worldpp.391-422, (2022)
Neurocomputing (2022): 108561-108561
Algorithmic Learning in a Random Worldpp.227-263, (2022)
Algorithmic Learning in a Random Worldpp.333-362, (2022)
Algorithmic Learning in a Random Worldpp.265-304, (2022)
Algorithmic Learning in a Random Worldpp.157-179, (2022)
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作者统计
#Papers: 141
#Citation: 5558
H-Index: 32
G-Index: 73
Sociability: 5
Diversity: 2
Activity: 2
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