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TFG_EST_Martín_Aleix.pdf (1.94 MB)

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Bachelor thesis

Publication date

2025

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cc-by-nc-nd (c) Martín Moral, 2025
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/225518

Generalized additive neural networks

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Abstract

Generalized Additive Neural Networks (GANNs) integrate the power of neural networks and the interpretability of Generalized Additive Models (GAMs). This thesis try to offer a comprehensive theoretical context, starting with the origins of linear models, following with generalized models and ending with the explanation GANNs. We review the mathematical formulation, advantages, and disadvantages of GANNs compared to traditional models and some nonparametric methods. In addition, we will present a real application that demonstrate how GANNs have a good predictive accuracy and a great model interpretability. This thesis shows how flexible GANNs are,and how they could support machine learning and statistical modeling to improve.

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Treballs Finals de Grau en Estadística UB-UPC, Facultat d'Economia i Empresa (UB) i Facultat de Matemàtiques i Estadística (UPC), Curs: 2024-2025, Tutor: Salvador Torra Porras

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Xarxes neuronals (Informàtica), Models matemàtics, Aprenentatge automàtic, Estadística, Treballs de fi de grau

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Neural networks (Computer science), Mathematical models, Machine learning, Statistics, Bachelor's theses

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Treballs Finals de Grau (TFG) - Estadística UB-UPC

Citation

MARTÍN MORAL, Aleix. Generalized additive neural networks. [consulted: 6 of June of 2026]. Available at: https://hdl.handle.net/2445/225518

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