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Sequential Linearithmic Time Optimal Unimodal Fitting When Minimizing Univariate Linear Losses

Year: 2023

Type: preprint

Abstract: This paper focuses on optimal unimodal transformation of the score outputs of a univariate learning model under linear loss functions. We demonstrate that the optimal mapping between score values and the target region is a rectangular function. To produce this optimal rectangular fit for the observed samples, we propose a sequential approach that can its estimation with each incoming new sample. Our approach has logarithmic time complexity per iteration and is optimally efficient.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2304.02141

Authors: Gokcesu, Kaan, Gokcesu, Hakan

Language: English

Topic: Machine Learning and Data Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4362679205

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