Now showing items 1-5 of 5

    • Bayesian Optimization Approaches for Massively Multi-modal Problems 

      Roman, I.; Mendiburu, A.; Santana, R.; Lozano, J.A.Autoridad BCAM (2019)
      The optimization of massively multi-modal functions is a challenging task, particularly for problems where the search space can lead the op- timization process to local optima. While evolutionary algorithms have been ...
    • Evolving Gaussian Process Kernels for Translation Editing Effort Estimation 

      Roman, I.; Santana, R.; Mendiburu, A.; Lozano, J.A.Autoridad BCAM (2019)
      In many Natural Language Processing problems the combination of machine learning and optimization techniques is essential. One of these problems is estimating the effort required to improve, under direct human supervision, ...
    • An Experimental Study in Adaptive Kernel Selection for Bayesian Optimization 

      Roman, I.; Santana, R.; Mendiburu, A.; Lozano, J.A.Autoridad BCAM (2019)
      Bayesian Optimization has been widely used along with Gaussian Processes for solving expensive-to-evaluate black-box optimization problems. Overall, this approach has shown good results, and particularly for parameter ...
    • in-depth analysis of SVM kernel learning and its components 

      Roman, I.; Santana, R.; Mendiburu, A.; Lozano, J.A.Autoridad BCAM (2020)
      The performance of support vector machines in non-linearly-separable classification problems strongly relies on the kernel function. Towards an automatic machine learning approach for this technique, many research outputs ...
    • Sentiment analysis with genetically evolved Gaussian kernels 

      Roman, I.; Santana, R.; Mendiburu, A.; Lozano, J.A.Autoridad BCAM (2019)
      Sentiment analysis consists of evaluating opinions or statements based on text analysis. Among the methods used to estimate the degree to which a text expresses a certain sentiment are those based on Gaussian Processes. ...