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Found 11 papers, 4 papers with code
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.
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.
An Efficient, Parallelized Algorithm for Optimal Conditional Entropy-Based Feature Selection
It consists of finding a subset of features that is optimum for a given cost function.
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 $\mathbb{L}(\mathcal{H})$.
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.
featsel: A framework for benchmarking of feature selection algorithms and cost functions
In this paper, we introduce featsel, a framework for benchmarking of feature selection algorithms and cost functions.
The U-curve optimization problem: improvements on the original algorithm and time complexity analysis
The U-curve optimization problem is characterized by a decomposable in U-shaped curves cost function over the chains of a Boolean lattice.
