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Open problems in one-parameter operator semigroups theory

Autor: Madou, Kodjo Raphaël, Remizov, Ivan, Vafadar, Reihaneh

One-parameter strongly continuous semigroups of linear bounded operators on Banach spaces (also known as $C_0$-semigroups) are a fundamental operator-theoretic tool used in the study of linear and non-linear evolution PDEs arising in physics, probabi

Externí odkaz: http://arxiv.org/abs/2410.00416

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Evaluating the Efficacy of Instance Incremental vs. Batch Learning in Delayed Label Environments: An Empirical Study on Tabular Data Streaming for Fraud Detection

Autor: Amekoe, Kodjo Mawuena, Lebbah, Mustapha, Jaffre, Gregoire, Azzag, Hanene, Dagdia, Zaineb Chelly

Real-world tabular learning production scenarios typically involve evolving data streams, where data arrives continuously and its distribution may change over time. In such a setting, most studies in the literature regarding supervised learning favor

Externí odkaz: http://arxiv.org/abs/2409.10111

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The Fixer: Visa Lottery Chronicles Piot Charles Batema Kodjo Nicolas

Autor: Nti, Kwaku

Publikováno v: Journal of Global South Studies, 2020 Oct 01. 37(2), 361-364.

Externí odkaz: https://www.jstor.org/stable/48660377

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The Fixer: Visa Lottery Chronicles Piot Charles Batema Kodjo Nicolas

Autor: Coe, Cati

Publikováno v: Anthropological Quarterly, 2020 Jul 01. 93(3), 559-562.

Externí odkaz: https://www.jstor.org/stable/48742617

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Convergence rates for Poisson learning to a Poisson equation with measure data

Autor: Bungert, Leon, Calder, Jeff, Mihailescu, Max, Houssou, Kodjo, Yuan, Amber

In this paper we prove discrete to continuum convergence rates for Poisson Learning, a graph-based semi-supervised learning algorithm that is based on solving the graph Poisson equation with a source term consisting of a linear combination of Dirac d

Externí odkaz: http://arxiv.org/abs/2407.06783

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