Zobrazeno 1 - 10
of 133
pro vyhledávání: '"Salcedo, Carlos"'
We present BALDUR, a novel Bayesian algorithm designed to deal with multi-modal datasets and small sample sizes in high-dimensional settings while providing explainable solutions. To do so, the proposed model combines within a common latent space the
Externí odkaz:
http://arxiv.org/abs/2411.07043
Autor:
Belenguer-Llorens, Albert, Sevilla-Salcedo, Carlos, Parrado-Hernández, Emilio, Gómez-Verdejo, Vanessa
This paper presents the Relevance Feature and Vector Machine (RFVM), a novel model that addresses the challenges of the fat-data problem when dealing with clinical prospective studies. The fat-data problem refers to the limitations of Machine Learnin
Externí odkaz:
http://arxiv.org/abs/2402.07079
Feature selection in noisy label scenarios remains an understudied topic. We propose a novel genetic algorithm-based approach, the Noise-Aware Multi-Objective Feature Selection Genetic Algorithm (NMFS-GA), for selecting optimal feature subsets in bin
Externí odkaz:
http://arxiv.org/abs/2401.06546
Multi-view datasets offer diverse forms of data that can enhance prediction models by providing complementary information. However, the use of multi-view data leads to an increase in high-dimensional data, which poses significant challenges for the p
Externí odkaz:
http://arxiv.org/abs/2305.18352
Autor:
Sevilla-Salcedo, Carlos, Gallardo-Antolín, Ascensión, Gómez-Verdejo, Vanessa, Parrado-Hernández, Emilio
This paper introduces a novel approach for multi-task regression that connects Kernel Machines (KMs) and Extreme Learning Machines (ELMs) through the exploitation of the Random Fourier Features (RFFs) approximation of the RBF kernel. In this sense, o
Externí odkaz:
http://arxiv.org/abs/2209.03028
Purpose: Handling heterogeneous and mixed data types has become increasingly critical with the exponential growth in real-world databases. While deep generative models attempt to merge diverse data views into a common latent space, they often sacrifi
Externí odkaz:
http://arxiv.org/abs/2207.09185
Publikováno v:
In Applied Soft Computing December 2024 167 Part B
Autor:
Sevilla-Salcedo, Carlos, Gallardo-Antolín, Ascensión, Gómez-Verdejo, Vanessa, Parrado-Hernández, Emilio
Publikováno v:
In Neural Networks November 2024 179
Autor:
Sevilla-Salcedo, Carlos, Imani, Vandad, Olmos, Pablo M., Gómez-Verdejo, Vanessa, Tohka, Jussi
Machine learning techniques typically applied to dementia forecasting lack in their capabilities to jointly learn several tasks, handle time dependent heterogeneous data and missing values. In this paper, we propose a framework using the recently pre
Externí odkaz:
http://arxiv.org/abs/2201.05040
Multi-view problems can be faced with latent variable models since they are able to find low-dimensional projections that fairly capture the correlations among the multiple views that characterise each datum. On the other hand, high-dimensionality an
Externí odkaz:
http://arxiv.org/abs/2006.00968