Her research interests lies in machine learning with a primary focus on model-based clustering. Yana Melnykov is an Assistant Professor of Applied Statistics at the University of Alabama. She received her Ph.D. degree from the University of Alabama in 2017. She also holds a M.S. degree in ...
For the case of linear value function approximations and λ e 0, the Least-Squares TD (LSTD) algorithm of Bradtke and Barto (1996, Machine learning, 22:1–3, 33–57) eliminates all stepsize parameters and improves data efficiency. This paper updates Bradtke and Barto's work in three ...
来源期刊 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2012 研究点推荐 mixture model Uniform Distributions multivariate statistics Model-Based Learning Mixture of Mixtures of Gaussian multivariate uniform distribution 0关于我们 百度学术集成海量学术资源,融合人工智能、深度学习、大数据分析等技术,...
More complexity of network structure is constructed by the deep learning models with the huge number of hidden layers and/or recurrent structure. This will provide a strong ability for learning and self-adaptive learning than the ability of the machine learning approaches. Thus, load forecasting ...
Parkinson’s disease (PD) exhibits significant clinical heterogeneity, presenting challenges in the identification of reliable electroencephalogram (EEG) biomarkers. Machine learning techniques have been integrated with resting-state EEG for PD diagnosis, but their practicality is constrained by the interpretab...
In Proc. 35th International Conference on Machine Learning 2323–2332 (PMLR, 2018). Shi, C. et al. GraphAF: a flow-based autoregressive model for molecular graph generation. Preprint at https://arxiv.org/abs/2001.09382 (2020). Gao, K., Nguyen, D. D., Tu, M. & Wei, G.-W. ...
[9,16]. Certain solutions focus on applying machine learning algorithms to the problem of cost and placement optimisation [12,14,17,18], especially the approaches that aim at allocating resources dynamically in a cloud environment, thus often using machine learning techniques to realise adaptivity....
Interpretability in machine learning models is important in high-stakes decisions such as whether to order a biopsy based on a mammographic exam. Mammography poses important challenges that are not present in other computer vision tasks: datasets are small, confounding information is present and it ca...
Machine learning project canvas (2019) https://www.mitsubishichem-hd.co.jp/news_release/pdf/190718.pdf Google Scholar Cited by (0) Hironori Takeuchiis a professor at Musashi University. He received M.S. degree in mathematical engineering from University of Tokyo in 2000. From 2000 to 2018,...
United States Patent US11599357 Note: If you have problems viewing the PDF, please make sure you have the latest version ofAdobe Acrobat. Back to full text