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)
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 in-dependences that hold in the distribution. Depending on the goal of learning intended by the user,(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 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)
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)
This work introduces Grow-Shrink with Search (GSS), a novel adaptation of the Grow-Shrink (GS) algorithm that learns a set of direct dependences of a random variable; called the Markov Blanket (MB) of the variable. We focus on the use of MBs for learning undirected probabilistic graphical models (a.k.a. Markov networks). As in the GS algorithm, GSS learns(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)
Markov networks are extensively used to model complex sequential, spatial, and relational interactions in a wide range of fields. By learning the structure of inde-pendences of a domain, more accurate joint probability distributions can be obtained for inference tasks or, more directly, for interpreting the most significant relations among the variables.(More)