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[2602.17985] Learning Without Training
Summary
This paper explores innovative methods in machine learning, addressing supervised learning, transfer learning, and classification through mathematical theory and novel algorithms.
Why It Matters
As machine learning continues to evolve, this research provides critical insights into improving function approximation, leveraging transfer learning, and enhancing classification accuracy. These advancements can significantly impact real-world applications across various domains, making it essential for researchers and practitioners in the field.
Key Takeaways
- Introduces a new method to improve function approximation in supervised learning.
- Explores transfer learning to enhance model performance across different domains.
- Proposes a novel approach to classification that unifies it with signal separation techniques.
Computer Science > Machine Learning arXiv:2602.17985 (cs) [Submitted on 20 Feb 2026] Title:Learning Without Training Authors:Ryan O'Dowd View a PDF of the paper titled Learning Without Training, by Ryan O'Dowd View PDF HTML (experimental) Abstract:Machine learning is at the heart of managing the real-world problems associated with massive data. With the success of neural networks on such large-scale problems, more research in machine learning is being conducted now than ever before. This dissertation focuses on three different projects rooted in mathematical theory for machine learning applications. The first project deals with supervised learning and manifold learning. In theory, one of the main problems in supervised learning is that of function approximation: that is, given some data set $\mathcal{D}=\{(x_j,f(x_j))\}_{j=1}^M$, can one build a model $F\approx f$? We introduce a method which aims to remedy several of the theoretical shortcomings of the current paradigm for supervised learning. The second project deals with transfer learning, which is the study of how an approximation process or model learned on one domain can be leveraged to improve the approximation on another domain. We study such liftings of functions when the data is assumed to be known only on a part of the whole domain. We are interested in determining subsets of the target data space on which the lifting can be defined, and how the local smoothness of the function and its lifting are related. The t...
