Testing and learning on distributional and set inputs
<p>As machine learning gains significant attention in many disciplines and research communities, the variety of data structures has increased, with examples including distributions and sets of observations. In this thesis, we consider sets and distributions as inputs for machine learning pr...
主要作者: | Law, H |
---|---|
其他作者: | Sejdinovic, D |
格式: | Thesis |
语言: | English |
出版: |
2019
|
主题: |
相似书籍
-
Utilizing Statistical Tests for Comparing Machine Learning Algorithms
由: Hozan Khalid Hamarashid
出版: (2021-07-01) -
The information of attribute uncertainties: what convolutional neural networks can learn about errors in input data
由: Natália V N Rodrigues, et al.
出版: (2023-01-01) -
Towards trustworthy machine learning with kernels
由: Chau, SL
出版: (2023) -
Towards data-efficient deep learning with meta-learning and symmetries
由: Xu, J
出版: (2023) -
Kernel-based hypothesis tests: large-scale approximations and Bayesian perspectives
由: Zhang, Q
出版: (2019)