Work

Classification and Regression Error Bounds for Inhomogenous Data With Applications to Wireless Networks

Year: 2024

Type: preprint

Abstract: In this paper, we study classification and regression error bounds for inhomogenous data that are independent but not necessarily identically distributed. First, we consider classification of data in the presence of non-stationary noise and establish ergodic type sufficient conditions that guarantee the achievability of the Bayes error bound, using universal rules. We then perform a similar analysis for $k$-nearest neighbour regression and obtain optimal error bounds for the same. Finally, we illustrate applications of our results in the context of wireless networks.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2404.02262

Author: Ganesan, Ghurumuruhan

Institution: Indian Institute of Science Education and Research, Bhopal

Language: English

Topic: Machine Learning and Algorithms

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Institute of Mathematical Sciences, Indian Institute of Science Education and Research Bhopal, Indian Institute of Science Education and Research Mohali

OpenAlex ID: W4393968008

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