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In this chapter, we study classification and regression error bounds for inhomogeneous data that are independent but not necessarily identically distributed. First, we consider classification of data in the presence of nonstationary 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.

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Classification and Regression Error Bounds for Inhomogeneous Data with Applications to Wireless Networks

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  • 发表日期：

    2026年

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  • Ghurumuruhan Ganesan

摘要

In this chapter, we study classification and regression error bounds for inhomogeneous data that are independent but not necessarily identically distributed. First, we consider classification of data in the presence of nonstationary 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.