Federico Schlüter

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In this work we consider the problem of learning the structure of Markov networks from data. We present an approach for tackling this problem called IBMAP, together with an efficient instantiation of the approach: the IBMAP-HC algorithm, designed for avoiding important limitations of existing independence-based algorithms. These algorithms proceed by(More)
Markov random fields provide a compact representation of joint probability distributions by representing its independence properties in an undirected graph. The well-known Hammersley-Clifford theorem uses these conditional independences to factorize a Gibbs distribution into a set of factors. However, an important issue of using a graph to represent(More)
The problem of learning the Markov network structure from data has become increasingly important in machine learning, and in many other application fields. Markov networks are probabilistic graphical models, a widely used formalism for handling probability distributions in intelligent systems. This document focuses on a technology called independence-based(More)
Learning the Markov network structure from data is a problem that has received considerable attention in machine learning, and in many other application fields. This work focuses on a particular approach for this purpose called Independence-Based learning. Such approach guarantees the learning of the correct structure efficiently, whenever data is(More)
This work focuses on learning the structure of Markov networks. Markov networks are parametric models for compactly representing complex probability distributions. These models are composed by: a structure and numerical weights. The structure describes independences that hold in the distribution. Depending on the goal of learning intended by the user,(More)
This work presents IBMAP, an approach for robust learning of Markov network structures from data, together with IBMAP-HC, an efficient instantiation of the approach. Existing Score-Based (SB) and Independence-Based (IB) approaches must make concessions either on robustness or efficiency. IBMAP-HC improves robustness efficiently through an IB-SB hybrid(More)
A new thermographic online system for quality control in the laminating process is presented in this paper. The developed online system allows a 100% control of the laminating process of wood-based panels. Different types of invisible defects can be selected for detection. The system is implemented on a standard PC and tested in an industrial production(More)
This work introduces the IB-score, a family of independence-based score functions for robust learning of Markov networks independence structures. Markov networks are a widely used graphical representation of probability distributions, with many applications in several fields of science. The main advantage of the IB-score is the possibility of computing it(More)
A massive amount of conditional independence tests on data must be performed in the problem of learning the structure of probabilistic graphical models when using the independence-based approach. An intermediate step in the computation of independence tests is the construction of contingency tables from the data. In this work we present an intelligent cache(More)