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On the Complexity of Labeled Datasets
The Statistical Learning Theory (SLT) provides the foundation to ensure that a supervised algorithm generalizes the mapping $f: \mathcal{X} \to \mathcal{Y}$ given $f$ is selected from its search space bias $\mathcal{F}$.
Coarse-Refinement Dilemma: On Generalization Bounds for Data Clustering
The Data Clustering (DC) problem is of central importance for the area of Machine Learning (ML), given its usefulness to represent data structural similarities from input spaces.
Computing the Shattering Coefficient of Supervised Learning Algorithms
The Statistical Learning Theory (SLT) provides the theoretical guarantees for supervised machine learning based on the Empirical Risk Minimization Principle (ERMP).
Providing theoretical learning guarantees to Deep Learning Networks
Deep Learning (DL) is one of the most common subjects when Machine Learning and Data Science approaches are considered.
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