Now showing items 1-6 of 6
in-depth analysis of SVM kernel learning and its components
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
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. ...
An Experimental Study in Adaptive Kernel Selection for Bayesian Optimization
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 ...
Evolving Gaussian Process Kernels for Translation Editing Effort Estimation
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, ...
Bayesian Optimization Approaches for Massively Multi-modal Problems
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 ...
perm mateda: A matlab toolbox of estimation of distribution algorithms for permutation-based combinatorial optimization problems
Permutation problems are combinatorial optimization problems whose solutions are naturally codified as permutations. Due to their complexity, motivated principally by the factorial cardinality of the search space of ...