Buscar
Mostrando ítems 1-6 de 6
Artículo
Ameva: An autonomous discretization algorithm
(ScienceDirect, 2009-04)
This paper describes a new discretization algorithm, called Ameva, which is designed to work with supervised learning algorithms. Ameva maximizes a contingency coefficient based on Chi-square statistics and generates a ...
Artículo
A new approach based on association rules to add explainability to time series forecasting models
(ScienceDirect, 2023)
Machine learning and deep learning have become the most useful and powerful tools in the last years to mine information from large datasets. Despite the successful application to many research fields, it is widely known ...
Artículo
Deep embeddings and Graph Neural Networks: using context to improve domain-independent predictions
(SprigerLink, 2023-06-28)
Graph neural networks (GNNs) are deep learning architectures that apply graph convolutions through message-passing processes between nodes, represented as embeddings. GNNs have recently become popular because of their ...
Artículo
Trip destination prediction based on past GPS log using a Hidden Markov Model
(Elsevier, 2010)
In this paper, a system based on the generation of a Hidden Markov Model from the past GPS log and cur- rent location is presented to predict a user’s destination when beginning a new trip. This approach dras- tically ...
Artículo
LEAPME: Learning-based Property Matching with Embeddings
(Cornell University, 2020)
Data integration tasks such as the creation and extension of knowledge graphs involve the fusion of heterogeneous entities from many sources. Matching and fusion of such entities require to also match and combine their ...
Artículo
Forecasting solar energy production in Spain: A comparison of univariate and multivariate models at the national level
(Elsevier, 2023-08-10)
Renewable energies, such as solar power, offer a clean and cost-effective energy source. However, their integration into national electricity grids poses challenges due to their dependence on climate and geography. While ...