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pro vyhledávání: '"Harikrishnan N"'
Inferring causal relationships in the decision-making processes of machine learning algorithms is a crucial step toward achieving explainable Artificial Intelligence (AI). In this research, we introduce a novel causality measure and a distance metric
Externí odkaz:
http://arxiv.org/abs/2411.01881
Decision Tree is a well understood Machine Learning model that is based on minimizing impurities in the internal nodes. The most common impurity measures are Shannon entropy and Gini impurity. These impurity measures are insensitive to the order of t
Externí odkaz:
http://arxiv.org/abs/2306.02617
Learning from limited and imbalanced data is a challenging problem in the Artificial Intelligence community. Real-time scenarios demand decision-making from rare events wherein the data are typically imbalanced. These situations commonly arise in med
Externí odkaz:
http://arxiv.org/abs/2205.06742
Discovering cause-effect from observational data is an important but challenging problem in science and engineering. In this work, a recently proposed brain inspired learning algorithm namely-\emph{Neurochaos Learning} (NL) is used for the classifica
Externí odkaz:
http://arxiv.org/abs/2201.12181
Publikováno v:
In Nuclear and Particle Physics Proceedings June 2024 344:19-21
Autor:
B, Harikrishnan N, Nagaraj, Nithin
The practical success of widely used machine learning (ML) and deep learning (DL) algorithms in Artificial Intelligence (AI) community owes to availability of large datasets for training and huge computational resources. Despite the enormous practica
Externí odkaz:
http://arxiv.org/abs/1905.12601
Akademický článek
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Publikováno v:
Langmuir. 39:6495-6504
Publikováno v:
International Journal of Human Movement and Sports Sciences. 11:276-284
Autor:
Harikrishnan, N. B.1,2 (AUTHOR) harikrishnannb@nias.res.in, Pranay, S. Y.2 (AUTHOR), Nagaraj, Nithin2 (AUTHOR)
Publikováno v:
Medical & Biological Engineering & Computing. Aug2022, Vol. 60 Issue 8, p2245-2255. 11p. 6 Charts, 4 Graphs.