All Outputs (2)

Decomposition-Fusion for Label Distribution Learning (2020)
Journal Article
González, M., González-Almagro, G., Triguero, I., Cano, J., & García, S. (2021). Decomposition-Fusion for Label Distribution Learning. Information Fusion, 66, 64-75. https://doi.org/10.1016/j.inffus.2020.08.024

Label Distribution Learning (LDL) is a general learning framework that assigns an instance to a distribution over a set of labels rather than to a single label or multiple labels. Current LDL methods have proven their effectiveness in many real-life... Read More about Decomposition-Fusion for Label Distribution Learning.

Evaluating Automated Machine Learning on Supervised Regression Traffic Forecasting Problems (2020)
Book Chapter
Angarita-Zapata, J. S., Masegosa, A. D., & Triguero, I. (2020). Evaluating Automated Machine Learning on Supervised Regression Traffic Forecasting Problems. In O. Llanes Santiago, C. Cruz Corona, A. J. Silva Neto, & J. L. Verdegay (Eds.), Computational intelligence in emerging technologies for engineering applications (187-204). Springer. https://doi.org/10.1007/978-3-030-34409-2_11

© Springer Nature Switzerland AG 2020. Traffic forecasting is a well-known strategy that supports road users and decision-makers to plan their movements on the roads and to improve the management of traffic, respectively. Current data availability an... Read More about Evaluating Automated Machine Learning on Supervised Regression Traffic Forecasting Problems.