Zobrazeno 1 - 10
of 364
pro vyhledávání: '"Cooper, Gregory F."'
Autor:
Sun, Yiming, Gao, Yuhe, Bao, Runxue, Cooper, Gregory F., Espino, Jessi, Hochheiser, Harry, Michaels, Marian G., Aronis, John M., Song, Chenxi, Ye, Ye
Transfer learning has become a pivotal technique in machine learning and has proven to be effective in various real-world applications. However, utilizing this technique for classification tasks with sequential data often faces challenges, primarily
Externí odkaz:
http://arxiv.org/abs/2402.01987
Directed acyclic graph (DAG) models have become widely studied and applied in statistics and machine learning -- indeed, their simplicity facilitates efficient procedures for learning and inference. Unfortunately, these models are not closed under ma
Externí odkaz:
http://arxiv.org/abs/2207.08963
Most causal discovery algorithms find causal structure among a set of observed variables. Learning the causal structure among latent variables remains an important open problem, particularly when using high-dimensional data. In this paper, we address
Externí odkaz:
http://arxiv.org/abs/2003.13135
Autor:
King, Andrew J., Angus, Derek C., Cooper, Gregory F., Mowery, Danielle L., Seaman, Jennifer B., Potter, Kelly M., Bukowski, Leigh A., Al-Khafaji, Ali, Gunn, Scott R., Kahn, Jeremy M.
Publikováno v:
In Journal of Biomedical Informatics October 2023 146
Autor:
Andrews, Bryan, Wongchokprasitti, Chirayu, Visweswaran, Shyam, Lakhani, Chirag M., Patel, Chirag J., Cooper, Gregory F.
Publikováno v:
In Artificial Intelligence In Medicine May 2023 139
Akademický článek
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Discovery of an accurate causal Bayesian network structure from observational data can be useful in many areas of science. Often the discoveries are made under uncertainty, which can be expressed as probabilities. To guide the use of such discoveries
Externí odkaz:
http://arxiv.org/abs/1712.08626
Publikováno v:
Journal of Clinical Oncology; 2024 Supplement, Vol. 42, p59-59, 48p
Learning accurate probabilistic models from data is crucial in many practical tasks in data mining. In this paper we present a new non-parametric calibration method called \textit{ensemble of near isotonic regression} (ENIR). The method can be consid
Externí odkaz:
http://arxiv.org/abs/1511.05191
Autor:
Visweswaran, Shyam, Cooper, Gregory F.
Learning Markov blanket (MB) structures has proven useful in performing feature selection, learning Bayesian networks (BNs), and discovering causal relationships. We present a formula for efficiently determining the number of MB structures given a ta
Externí odkaz:
http://arxiv.org/abs/1407.2483