Quantum kernel methods show promise for accelerating data analysis by efficiently learning relationships between input data points that have been encoded into an exponentially large Hilbert space. While this technique has been used successfully in small-
Machine learning for high-dimensional dynamic stochastic economies. Available at SSRN: https://ssrn.com/abstract=2927400 , 1-43.Scheidegger, S., and I. Bilionis (2017): "Machine Learning for High-Dimensional Dy- namic Stochastic Economies," UCL....
The role of Artificial Intelligence and Machine Learning in cancer research offers several advantages, primarily scaling up the information processing and increasing the accuracy of the clinical decision-making. The key enabling tools currently in use in Precision, Digital and Translational Medicine, here...
A Comparison of Cox Model and Machine Learning Techniques in the High-Dimensional Survival Data Predicting certain events of interest that will occur at future time points is the primary objective of survival analysis. Machine Learning (ML) algorithms... S Suresh,P Divya,M Ramadurai - Internationa...
We aimed to develop an optimal dosing algorithm for vancomycin based on the high-dimensional data using the proposed variable engineering and machine-learning methods. METHODS :This study proposed a variable engineering process that automatically generates second-order variable interactions. We performed ...
1 Applications of Machine Learning and High Dimensional Visualization in Cancer Diagnosis and Detection John F. McCarthy*, Kenneth A. Marx, Patrick Hoff et al., “Applications of machine learning and high-dimensional visualization in cancer detection, diagnosis, and management - McCarthy, Marx, et...
Introduction: This big data era has witnessed a rapid increase in the volume of data. Together with the increasement of volume, the amount of available information of each data sample increases even faster, resulting in a high-dimensionalregime. In machine learning/statistics filed, we typically ...
Private Convex Empirical Risk Minimization and High-dimensional Regression Privacy-preserving machine learning algorithms are crucial for the increasingly common setting in which personal data, such as medical or financial records... Daniel Kifer,Adam Smith,Abhradeep Thakurta - JMLR Workshop and Conference...
Finally, comparison with benchmark models shows that the prediction model can achieve desirable prediction results and thus effectively solve the challenge of predictions based on high-dimensional and imbalanced data. 展开 关键词: Default prediction High-dimensional data Imbalanced data Machine learning ...
Learning from models: high-dimensional analyses on the performance of machine learning interatomic potentials Yunsheng Liu & Yifei Mo npj Computational Materials volume 10, Article number: 159 (2024) Cite this article 2325 Accesses 1 Citations 1 Altmetric Metrics details Abstract...