Search Results for author:
Found 12 papers, 2 papers with code
Metastable Financial Markets
Metastability is a phenomenon observed in stochastic systems which stay in a false-equilibrium within a region of its state space until the occurrence of a sequence of rare events that leads to an abrupt transition to a different region.
The Lattice Overparametrization Paradigm for the Machine Learning of Lattice Operators
The machine learning of lattice operators has three possible bottlenecks.
An Algorithm to Train Unrestricted Sequential Discrete Morphological Neural Networks
We also proposed a stochastic lattice descent algorithm (SLDA) to learn the parameters of Canonical Discrete Morphological Neural Networks (CDMNN), whose architecture is composed only of operators that can be decomposed as the supremum, infimum, and complement of erosions and dilations.
Discrete Morphological Neural Networks
We propose the Discrete Morphological Neural Networks (DMNN) for binary image analysis to represent W-operators and estimate them via machine learning.
Distribution-free Deviation Bounds of Learning via Model Selection with Cross-validation Risk Estimation
Cross-validation techniques for risk estimation and model selection are widely used in statistics and machine learning.
The role of prior information and computational power in Machine Learning
Science consists on conceiving hypotheses, confronting them with empirical evidence, and keeping only hypotheses which have not yet been falsified.
Learning the hypotheses space from data through a U-curve algorithm
This paper proposes a data-driven systematic, consistent and non-exhaustive approach to Model Selection, that is an extension of the classical agnostic PAC learning model.
Parameter estimation in dynamical systems via Statistical Learning: a reinterpretation of Approximate Bayesian Computation applied to COVID-19 spread
We propose a robust parameter estimation method for dynamical systems based on Statistical Learning techniques which aims to estimate a set of parameters that well fit the dynamics in order to obtain robust evidences about the qualitative behaviour of its trajectory.
Learning the Hypotheses Space from data Part II: Convergence and Feasibility
In this paper, we carry further our agenda, by showing the consistency of a model selection framework based on Learning Spaces, in which one selects from data the Hypotheses Space on which to learn.
Learning the Hypotheses Space from data: Learning Space and U-curve Property
A remarkable, formally proved, consequence of this approach are conditions on $\mathbb{L}(\mathcal{H})$ and on the loss function that lead to estimated out-of-sample error surfaces which are true U-curves on $\mathbb{L}(\mathcal{H})$ chains, enabling a more efficient search on $\mathbb{L}(\mathcal{H})$.
A survey of a hurdle model for heavy-tailed data based on the generalized lambda distribution
In this survey we present an extensive research of the vast literature about the Generalized Lambda Distribution (GLD) and propose a hurdle, or two-way, model whose associated distribution is the GLD in order to meet the demand for a highly flexible model of heavy-tailed data with excess of zeros.
Applications 62P05
Feature Selection based on the Local Lift Dependence Scale
The main contribution of this paper is to define and apply this local measure, which permits to analyse local properties of joint distributions that are neglected by the classical Shanon's global measure.
