arXiv:2307.11925v3 Announce Type: replace Abstract: To present Mercer large-scale kernel machines from a ridge function perspective, we recall the results by Lin and Pinkus from {\it Fundamentality of ridge functions}. We consider the main result of the recent paper by Rachimi and Recht, 2008, {\it Random features for large-scale kernel machines} from the Approximation Theory point of view. We study which kernels could be approximated by a sum of products of cosine functions with arguments depending on $x$ and $y$ and present the obstacles of such an approach. The results of this article are a
The paper contributes to ongoing research into making large-scale kernel machines more efficient and understandable, building on previous foundational work in approximation theory.
Improved theoretical understanding of kernel methods can lead to more robust, scalable, and interpretable AI models, impacting various applications reliant on machine learning.
This research provides new theoretical insights into the approximability of kernels, potentially guiding future algorithmic developments for large-scale machine learning.
- · Machine Learning Researchers
- · AI Developers
- · High-Performance Computing
- · Inefficient AI algorithms
Further theoretical development in kernel methods, potentially leading to more efficient algorithms.
Improved performance and scalability of AI systems that rely on kernel-based techniques.
Broader adoption of sophisticated machine learning models in resource-constrained environments due to enhanced efficiency.
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