1,721,670 research outputs found
Unsupervised Nonparametric Density Estimation: A Neural Network Approach
No full textOne major problem in pattern recognition is estimating probability density functions. Unfortunately, parametric techniques rely on an arbitrary assumption on the form of the underlying, unknown density function. On the other hand, nonparametric techniques, such as the popular k(n)-Nearest Neighbor (not to be confused with the k-Nearest Neighbor classification algorithm), allow to remove such an assumption. Albeit effective, the k(n)-Nearest Neighbor is affected by a number of limitations. Artificial neural networks are, in principle, an alternative family of nonpararnetric models. So far, artificial neural networks have been extensively used to estimate probabilities (e.g., class-posterior probabilities). However, they have not been exploited to estimate instead probability density functions. This paper introduces a simple, neural-based algorithm for unsupervised, nonparametric estimation of multivariate densities, relying on the k(n)-Nearest Neighbor technique. This approach overcomes the limitations of k(n)-Nearest Neighbor, possibly improving the estimation accuracy of the resulting pdf models. An experimental investigation of the algorithm behavior is offered, exploiting random samples drawn from a mixture of Fisher-Tippett density functions
Hybrid Random Fields
Full text linkThis book presents an exciting new synthesis of directed and undirected, discrete and continuous graphical models. Combining elements of Bayesian networks and Markov random fields, the newly introduced hybrid random fields are an interesting approach to get the best of both these worlds, with an added promise of modularity and scalability. The authors have written an enjoyable book---rigorous in the treatment of the mathematical background, but also enlivened by interesting and original historical and philosophical perspectives. -- Manfred Jaeger, Aalborg Universitet The book not only marks a
Scalable Statistical Learning: A Modular Bayesian/Markov Network Approach
No full textIn this paper we propose a hybrid probabilistic graphical model for pseudo-likelihood estimation in highdimensional domains. The model is based on Bayesian networks and Markov random fields. On the one hand, we prove that the proposed model is more expressive than Bayesian networks in terms of the representable distributions. On the other hand, we develop a computationally efficient structure learning algorithm, and we provide theoretical and experimental evidence showing how the modular nature of our model allows structure learning to scale up very well to high-dimensional datasets. The capability of the hybrid model to accurately learn complex networks of conditional independencies is illustrated by promising results in pattern recognition applications. ©2009 IEEE
Scalable pseudo-likelihood estimation in hybrid random fields
No full textLearning probabilistic graphical models from high-dimensional datasets is a computationally challenging task. In many interesting applications, the domain dimensionality is such as to prevent state-of-the-art statistical learning techniques from delivering accurate models in reasonable time. This paper presents a hybrid random field model for pseudo-likelihood estimation in high-dimensional domains. A theoretical analysis proves that the class of pseudo-likelihood distributions representable by hybrid random fields strictly includes the class of joint probability distributions representable by Bayesian networks. In order to learn hybrid random fields from data, we develop the Markov Blanket Merging algorithm. Theoretical and experimental evidence shows that Markov Blanket Merging scales up very well to high-dimensional datasets. As compared to other widely used statistical learning techniques, Markov Blanket Merging delivers accurate results in a number of link prediction tasks, while achieving also significant improvements in terms of computational efficiency.
Our software implementation of the models investigated in this paper is publicly available at http://www.dii.unisi.it/~freno/. The same website also hosts the datasets used in this work that are not available elsewhere in the same preprocessing used for our experiments
Dynamic hybrid random fields for the probabilistic graphical modeling of sequential data: definitions, algorithms, and an application to bioinformatics
No full textThe paper introduces a dynamic extension of the hybrid random field (HRF), called dynamic HRF (D-HRF). The D-HRF is aimed at the probabilistic graphical modeling of arbitrary-length sequences of sets of (time-dependent) discrete random variables under Markov assumptions. Suitable maximum likelihood algorithms for learning the parameters and the structure of the D-HRF are presented. The D-HRF inherits the computational efficiency and the modeling capabilities of HRFs, subsuming both dynamic Bayesian networks and Markov random fields. The behavior of the D-HRF is first evaluated empirically on synthetic data drawn from probabilistic distributions having known form. Then, D-HRFs (combined with a recurrent autoencoder) are successfully applied to the prediction of the disulfide-bonding state of cysteines from the primary structure of proteins in the Protein Data Bank
Probabilistic interpretation of neural networks for the classification of vectors, sequences and graphs
No full textThis chapter introduces a probabilistic interpretation of artificial neural networks (ANNs), moving the focus from posterior probabilities to probability density functions (pdfs). Parametric and non-parametric neural-based algorithms for unsupervised estimation of pdfs, relying on maximum-likelihood or on the Parzen Window techniques, are reviewed. The approaches may overcome the limitations of traditional statistical estimation methods, possibly leading to improved pdf models. Two paradigms for combining ANNs and hidden Markov models (HMMs) for sequence recognition are then discussed. These models rely on (i) an ANN that estimates state-posteriors over a maximum-a-posteriori criterion, or on (ii) a connectionist estimation of emission pdfs, featuring global optimization of HMM and ANN parameters over a maximum-likelihood criterion. Finally, the chapter faces the problem of the classification of graphs (structured data), by presenting a connectionist probabilistic model for the posterior probability of classes given a labeled graphical pattern. In all cases, empirical evidence and theoretical arguments underline the fact that plausible probabilistic interpretations of ANNs are viable and may lead to improved statistical classifiers, not only in the statical but also in the sequential and structured pattern recognition setups
La constituyente: ¿Un freno a la reactivación económica?
No full textEn este análisis, abordamos el proceso de la Asamblea Constituyente desde una perspectiva académica y constitucional. De igual manera, confrontamos la visión jurídica y política del proceso con la necesidad imperiosa de la recuperación y reactivación económica. Esta confrontación nos invita a discutir en diversos ámbitos de la economía y la hacienda pública la necesidad de encontrar un equilibrio entre ambas prioridades, sin caer en la falsa dicotomía de "hacer lo uno y dejar de hacer lo otro"
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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