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RECERCAT Home > Universitat Politècnica de Catalunya > Documents de recerca > View document

To access the full text documents, please follow this link: http://hdl.handle.net/2117/18311

Title: Making machine learning models interpretable
Author: Vellido Alcacena, Alfredo; Martin Guerrero, Jose D.; Lisboa, Paulo J.G.
Other authors: Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics; Universitat Politècnica de Catalunya. SOCO - Soft Computing
Abstract:
Subject(s): -Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
-Artificial intelligence
-Machine learning
-Intel·ligència artificial
-Aprenentatge automàtic
Rights:
Document type: Article - Published version
Conference Object
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Making nonlinear manifold learning models interpretable: the manifold grand tour
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Seeing is believing: the importance of visualization in real-world machine learning applications
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Classification, dimensionality reduction, and maximally discriminatory visualization of a multicentre 1H-MRS database of brain tumors
Lisboa, Paulo J.G.; Romero Merino, Enrique; Vellido Alcacena, Alfredo; Julià Sapé, Margarida; Arús, Carles
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