The scholarly networks of Albrecht von Haller and the Economic Society of Bern – a multi-level network analysis

Here, by the example of the transfer of cultivated plants in the context of the correspondence networks of Albrecht von Haller and the Economic Society, a multi-level network analysis is suggested. By a multi-level procedure, the chronological dynamics, the social structure, the spatial distribution and the functional networking are analyzed one after the other. These four levels of network analysis do not compete with each other but are mutually supporting. This aims at a deeper understanding of how these networks contributed to an international transfer of knowledge in the 18th century.

Interaction of Plectin with Keratins 5 and 14: Dependence on Several Plectin Domains and Keratin Quaternary Structure.

Plectin, a cytolinker of the plakin family, anchors the intermediate filament (IF) network formed by keratins 5 and 14 (K5/K14) to hemidesmosomes, junctional adhesion complexes in basal keratinocytes. Genetic alterations of these proteins cause epidermolysis bullosa simplex (EBS) characterized by disturbed cytoarchitecture and cell fragility. The mechanisms through which mutations located after the documented plectin IF-binding site, composed of the plakin-repeat domain (PRD) B5 and the linker, as well as mutations in K5 or K14, lead to EBS remain unclear. We investigated the interaction of plectin C terminus, encompassing four domains, the PRD B5, the linker, the PRD C, and the C extremity, with K5/K14 using different approaches, including a rapid and sensitive fluorescent protein-binding assay, based on enhanced green fluorescent protein-tagged proteins (FluoBACE). Our results demonstrate that all four plectin C-terminal domains contribute to its association with K5/K14 and act synergistically to ensure efficient IF binding. The plectin C terminus predominantly interacted with the K5/K14 coil 1 domain and bound more extensively to K5/K14 filaments compared with monomeric keratins or IF assembly intermediates. These findings indicate a multimodular association of plectin with K5/K14 filaments and give insights into the molecular basis of EBS associated with pathogenic mutations in plectin, K5, or K14 genes.Journal of Investigative Dermatology advance online publication, 10 July 2014; doi:10.1038/jid.2014.255.

Synthesis, structure and magnetic properties of cobalt(II) and copper(II) coordination polymers assembled by phthalate and 4-methylimidazole

New coordination polymers [M(Pht)(4-MeIm)2(H2O)]n (M=Co (1), Cu (2); Pht2−=dianion of o-phthalic acid; 4-MeIm=4-methylimidazole) have been synthesized and characterized by IR spectroscopy, X-ray crystallography, thermogravimetric analysis and magnetic measurements. The crystal structures of 1 and 2 are isostructural and consist of [M(4-MeIm)2(H2O)] building units linked in infinite 1D helical chains by 1,6-bridging phthalate ions which also act as chelating ligands through two O atoms from one carboxylate group in the case of 1. In complex 1, each Co(II) atom adopts a distorted octahedral N2O4 geometry being coordinated by two N atoms from two 4-MeIm, three O atoms of two phthalate residues and one O atom of a water molecule, whereas the square-pyramidal N2O3 coordination of the Cu(II) atom in 2 includes two N atoms of N-containing ligands, two O atoms of two carboxylate groups from different Pht, and a water molecule. An additional strong O–H⋯O hydrogen bond between a carboxylate group of the phthalate ligand and a coordinated water molecule join the 1D helical chains to form a 2D network in both compounds. The thermal dependences of the magnetic susceptibilities of the polymeric helical Co(II) chain compound 1 were simulated within the temperature range 20–300 K as a single ion case, whereas for the Cu(II) compound 2, the simulations between 25 and 300 K, were made for a linear chain using the Bonner–Fisher approximation. Modelling the experimental data of compound 1 with MAGPACK resulted in: g=2.6, |D|=62 cm−1. Calculations using the Bonner–Fisher approximation gave the following result for compound 2: g=2.18, J=–0.4 cm−1.

A Thalamic-Fronto-Parietal Structural Covariance Network Emerging in the Course of Recovery from Hand Paresis after Ischemic Stroke.

AIM To describe structural covariance networks of gray matter volume (GMV) change in 28 patients with first-ever stroke to the primary sensorimotor cortices, and to investigate their relationship to hand function recovery and local GMV change. METHODS Tensor-based morphometry maps derived from high-resolution structural images were subject to principal component analyses to identify the networks. We calculated correlations between network expression and local GMV change, sensorimotor hand function and lesion volume. To verify which of the structural covariance networks of GMV change have a significant relationship to hand function, we performed an additional multivariate regression approach. RESULTS Expression of the second network, explaining 9.1% of variance, correlated with GMV increase in the medio-dorsal (md) thalamus and hand motor skill. Patients with positive expression coefficients were distinguished by significantly higher GMV increase of this structure during stroke recovery. Significant nodes of this network were located in md thalamus, dorsolateral prefrontal cortex, and higher order sensorimotor cortices. Parameter of hand function had a unique relationship to the network and depended on an interaction between network expression and lesion volume. Inversely, network expression is limited in patients with large lesion volumes. CONCLUSION Chronic phase of sensorimotor cortical stroke has been characterized by a large scale co-varying structural network in the ipsilesional hemisphere associated specifically with sensorimotor hand skill. Its expression is related to GMV increase of md thalamus, one constituent of the network, and correlated with the cortico-striato-thalamic loop involved in control of motor execution and higher order sensorimotor cortices. A close relation between expression of this network with degree of recovery might indicate reduced compensatory resources in the impaired subgroup.

The European Network for Translational Research in Atrial Fibrillation (EUTRAF): objectives and initial results

Atrial fibrillation (AF) is the most common sustained arrhythmia in the general population. As an age-related arrhythmia AF is becoming a huge socio-economic burden for European healthcare systems. Despite significant progress in our understanding of the pathophysiology of AF, therapeutic strategies for AF have not changed substantially and the major challenges in the management of AF are still unmet. This lack of progress may be related to the multifactorial pathogenesis of atrial remodelling and AF that hampers the identification of causative pathophysiological alterations in individual patients. Also, again new mechanisms have been identified and the relative contribution of these mechanisms still has to be established. In November 2010, the European Union launched the large collaborative project EUTRAF (European Network of Translational Research in Atrial Fibrillation) to address these challenges. The main aims of EUTRAF are to study the main mechanisms of initiation and perpetuation of AF, to identify the molecular alterations underlying atrial remodelling, to develop markers allowing to monitor this processes, and suggest strategies to treat AF based on insights in newly defined disease mechanisms. This article reports on the objectives, the structure, and initial results of this network.

Inefficient Preparatory fMRI-BOLD Network Activations Predict Working Memory Dysfunctions in Patients with Schizophrenia

Patients with schizophrenia show abnormal dynamics and structure of temporally ­coherent networks (TCNs) assessed using fMRI, which undergo adaptive shifts in preparation for a cognitively demanding task. During working memory (WM) tasks, patients with schizophrenia show persistent deficits in TCNs as well as EEG indices of WM. Studying their temporal relationship during WM tasks might provide novel insights into WM performance deficits seen in schizophrenia. Simultaneous EEG-fMRI data were acquired during the performance of a verbal Sternberg WM task with two load levels (load 2 and load 5) in 17 patients with schizophrenia and 17 matched healthy controls. Using covariance mapping, we investigated the relationship of the activity in the TCNs before the memoranda were encoded and EEG spectral power during the retention interval. We assessed four TCNs – default mode network (DMN), dorsal attention network (dAN), left and right working memory networks (WMNs) – and three EEG bands – theta, alpha, and beta. In healthy controls, there was a load-dependent inverse relation between DMN and frontal midline theta power and an anti-correlation between DMN and dAN. Both effects were not significantly detectable in patients. In addition, healthy controls showed a left-lateralized load-dependent recruitment of the WMNs. Activation of the WMNs was bilateral in patients, suggesting more resources were recruited for successful performance on the WM task. Our findings support the notion of schizophrenia patients showing deviations in their neurophysiological responses before the retention of relevant information in a verbal WM task. Thus, treatment strategies as neurofeedback ­targeting prestates could be beneficial as task performance relies on the preparatory state of the brain.

The interorganizational implementation of state health policy: A network approach

This is an implementation analysis of three consecutive state health policies whose goal was to improve access to maternal and child health services in Texas from 1983 to 1986. Of particular interest is the choice of the unit of analysis, the policy subsystem, and the network approach to analysis. The network approach analyzes and compares the structure and decision process of six policy subsystems in order to explain program performance. Both changes in state health policy as well as differences in implementation contexts explain evolution of the program administrative and service unit, the policy subsystem. And, in turn, the evolution of the policy subsystem explains changes in program performance. ^

Learning an L1-regularized Gaussian Bayesian Network in the Equivalence Class Space

Learning the structure of a graphical model from data is a common task in a wide range of practical applications. In this paper, we focus on Gaussian Bayesian networks, i.e., on continuous data and directed acyclic graphs with a joint probability density of all variables given by a Gaussian. We propose to work in an equivalence class search space, specifically using the k-greedy equivalence search algorithm. This, combined with regularization techniques to guide the structure search, can learn sparse networks close to the one that generated the data. We provide results on some synthetic networks and on modeling the gene network of the two biological pathways regulating the biosynthesis of isoprenoids for the Arabidopsis thaliana plant

Conflict and cognitive control during sentence comprehension: recruitment of a frontal network during the processing of Spanish object-first sentences.

During sentence processing there is a preference to treat the first noun phrase found as the subject and agent, unless marked the other way. This preference would lead to a conflict in thematic role assignment when the syntactic structure conforms to a non-canonical object-before-subject pattern. Left perisylvian and fronto-parietal brain networks have been found to be engaged by increased computational demands during sentence comprehension, while event-reated brain potentials have been used to study the on-line manifestation of these demands. However, evidence regarding the spatiotemporal organization of brain networks in this domain is scarce. In the current study we used Magnetoencephalography to track spatio-temporally brain activity while Spanish speakers were reading subject- and object-first cleft sentences. Both kinds of sentences remained ambiguous between a subject-first or an object-first interpretation up to the appearance of the second argument. Results show the time-modulation of a frontal network at the disambiguation point of object-first sentences. Moreover, the time windows where these effects took place have been previously related to thematic role integration (300–500 ms) and to sentence reanalysis and resolution of conflicts during processing (beyond 500 ms post-stimulus). These results point to frontal cognitive control as a putative key mechanism which may operate when a revision of the sentence structure and meaning is necessary

6x3 Microstrip Beam Forming Network for Multibeam Triangular Array

A six inputs and three outputs structure which can be used to obtain six simultaneous beams with a triangular array of 3 elements is presented. The beam forming network is obtained combining balanced and unbalanced hybrid couplers and allows to obtain six main beams with sixty degrees of separation in azimuth direction. Simulations and measurements showing the performance of the array and other detailed results are presented

Community Structure in Soil Porous System

Diffusion controls the gaseous transport process in soils when advective transport is almost null. Knowledge of the soil structure and pore connectivity are critical issues to understand and modelling soil aeration, sequestration or emission of greenhouse gasses, volatilization of volatile organic chemicals among other phenomena. In the last decades these issues increased our attention as scientist have realize that soil is one of the most complex materials on the earth, within which many biological, physical and chemical processes that support life and affect climate change take place. A quantitative and explicit characterization of soil structure is difficult because of the complexity of the pore space. This is the main reason why most theoretical approaches to soil porosity are idealizations to simplify this system. In this work, we proposed a more realistic attempt to capture the complexity of the system developing a model that considers the size and location of pores in order to relate them into a network. In the model we interpret porous soils as heterogeneous networks where pores are represented by nodes, characterized by their size and spatial location, and the links representing flows between them. In this work we perform an analysis of the community structure of porous media of soils represented as networks. For different real soils samples, modelled as heterogeneous complex networks, spatial communities of pores have been detected depending on the values of the parameters of the porous soil model used. These types of models are named as Heterogeneous Preferential Attachment (HPA). Developing an exhaustive analysis of the model, analytical solutions are obtained for the degree densities and degree distribution of the pore networks generated by the model in the thermodynamic limit and shown that the networks exhibit similar properties to those observed in other complex networks. With the aim to study in more detail topological properties of these networks, the presence of soil pore community structures is studied. The detection of communities of pores, as groups densely connected with only sparser connections between groups, could contribute to understand the mechanisms of the diffusion phenomena in soils.

Structural health monitoring network system with wireless communications inside closed aerospace structures

Structural Health Monitoring (SHM) requires integrated "all in one" electronic devices capable of performing analysis of structural integrity and on-board damage detection in aircraft?s structures. PAMELA III (Phased Array Monitoring for Enhanced Life Assessment, version III) SHM embedded system is an example of this device type. This equipment is capable of generating excitation signals to be applied to an array of integrated piezoelectric Phased Array (PhA) transducers stuck to aircraft structure, acquiring the response signals, and carrying out the advanced signal processing to obtain SHM maps. PAMELA III is connected with a host computer in order to receive the configuration parameters and sending the obtained SHM maps, alarms and so on. This host can communicate with PAMELA III through an Ethernet interface. To avoid the use of wires where necessary, it is possible to add Wi-Fi capabilities to PAMELA III, connecting a Wi-Fi node working as a bridge, and to establish a wireless communication between PAMELA III and the host. However, in a real aircraft scenario, several PAMELA III devices must work together inside closed structures. In this situation, it is not possible for all PAMELA III devices to establish a wireless communication directly with the host, due to the signal attenuation caused by the different obstacles of the aircraft structure. To provide communication among all PAMELA III devices and the host, a wireless mesh network (WMN) system has been implemented inside a closed aluminum wingbox. In a WMN, as long as a node is connected to at least one other node, it will have full connectivity to the entire network because each mesh node forwards packets to other nodes in the network as required. Mesh protocols automatically determine the best route through the network and can dynamically reconfigure the network if a link drops out. The advantages and disadvantages on the use of a wireless mesh network system inside closed aerospace structures are discussed.

Contributions to Bayesian network learning with applications to neuroscience

Neuronal morphology is a key feature in the study of brain circuits, as it is highly related to information processing and functional identification. Neuronal morphology affects the process of integration of inputs from other neurons and determines the neurons which receive the output of the neurons. Different parts of the neurons can operate semi-independently according to the spatial location of the synaptic connections. As a result, there is considerable interest in the analysis of the microanatomy of nervous cells since it constitutes an excellent tool for better understanding cortical function. However, the morphologies, molecular features and electrophysiological properties of neuronal cells are extremely variable. Except for some special cases, this variability makes it hard to find a set of features that unambiguously define a neuronal type. In addition, there are distinct types of neurons in particular regions of the brain. This morphological variability makes the analysis and modeling of neuronal morphology a challenge. Uncertainty is a key feature in many complex real-world problems. Probability theory provides a framework for modeling and reasoning with uncertainty. Probabilistic graphical models combine statistical theory and graph theory to provide a tool for managing domains with uncertainty. In particular, we focus on Bayesian networks, the most commonly used probabilistic graphical model. In this dissertation, we design new methods for learning Bayesian networks and apply them to the problem of modeling and analyzing morphological data from neurons. The morphology of a neuron can be quantified using a number of measurements, e.g., the length of the dendrites and the axon, the number of bifurcations, the direction of the dendrites and the axon, etc. These measurements can be modeled as discrete or continuous data. The continuous data can be linear (e.g., the length or the width of a dendrite) or directional (e.g., the direction of the axon). These data may follow complex probability distributions and may not fit any known parametric distribution. Modeling this kind of problems using hybrid Bayesian networks with discrete, linear and directional variables poses a number of challenges regarding learning from data, inference, etc. In this dissertation, we propose a method for modeling and simulating basal dendritic trees from pyramidal neurons using Bayesian networks to capture the interactions between the variables in the problem domain. A complete set of variables is measured from the dendrites, and a learning algorithm is applied to find the structure and estimate the parameters of the probability distributions included in the Bayesian networks. Then, a simulation algorithm is used to build the virtual dendrites by sampling values from the Bayesian networks, and a thorough evaluation is performed to show the model’s ability to generate realistic dendrites. In this first approach, the variables are discretized so that discrete Bayesian networks can be learned and simulated. Then, we address the problem of learning hybrid Bayesian networks with different kinds of variables. Mixtures of polynomials have been proposed as a way of representing probability densities in hybrid Bayesian networks. We present a method for learning mixtures of polynomials approximations of one-dimensional, multidimensional and conditional probability densities from data. The method is based on basis spline interpolation, where a density is approximated as a linear combination of basis splines. The proposed algorithms are evaluated using artificial datasets. We also use the proposed methods as a non-parametric density estimation technique in Bayesian network classifiers. Next, we address the problem of including directional data in Bayesian networks. These data have some special properties that rule out the use of classical statistics. Therefore, different distributions and statistics, such as the univariate von Mises and the multivariate von Mises–Fisher distributions, should be used to deal with this kind of information. In particular, we extend the naive Bayes classifier to the case where the conditional probability distributions of the predictive variables given the class follow either of these distributions. We consider the simple scenario, where only directional predictive variables are used, and the hybrid case, where discrete, Gaussian and directional distributions are mixed. The classifier decision functions and their decision surfaces are studied at length. Artificial examples are used to illustrate the behavior of the classifiers. The proposed classifiers are empirically evaluated over real datasets. We also study the problem of interneuron classification. An extensive group of experts is asked to classify a set of neurons according to their most prominent anatomical features. A web application is developed to retrieve the experts’ classifications. We compute agreement measures to analyze the consensus between the experts when classifying the neurons. Using Bayesian networks and clustering algorithms on the resulting data, we investigate the suitability of the anatomical terms and neuron types commonly used in the literature. Additionally, we apply supervised learning approaches to automatically classify interneurons using the values of their morphological measurements. Then, a methodology for building a model which captures the opinions of all the experts is presented. First, one Bayesian network is learned for each expert, and we propose an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts is induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts is built. A thorough analysis of the consensus model identifies different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types can be defined by performing inference in the Bayesian multinet. These findings are used to validate the model and to gain some insights into neuron morphology. Finally, we study a classification problem where the true class label of the training instances is not known. Instead, a set of class labels is available for each instance. This is inspired by the neuron classification problem, where a group of experts is asked to individually provide a class label for each instance. We propose a novel approach for learning Bayesian networks using count vectors which represent the number of experts who selected each class label for each instance. These Bayesian networks are evaluated using artificial datasets from supervised learning problems. Resumen La morfología neuronal es una característica clave en el estudio de los circuitos cerebrales, ya que está altamente relacionada con el procesado de información y con los roles funcionales. La morfología neuronal afecta al proceso de integración de las señales de entrada y determina las neuronas que reciben las salidas de otras neuronas. Las diferentes partes de la neurona pueden operar de forma semi-independiente de acuerdo a la localización espacial de las conexiones sinápticas. Por tanto, existe un interés considerable en el análisis de la microanatomía de las células nerviosas, ya que constituye una excelente herramienta para comprender mejor el funcionamiento de la corteza cerebral. Sin embargo, las propiedades morfológicas, moleculares y electrofisiológicas de las células neuronales son extremadamente variables. Excepto en algunos casos especiales, esta variabilidad morfológica dificulta la definición de un conjunto de características que distingan claramente un tipo neuronal. Además, existen diferentes tipos de neuronas en regiones particulares del cerebro. La variabilidad neuronal hace que el análisis y el modelado de la morfología neuronal sean un importante reto científico. La incertidumbre es una propiedad clave en muchos problemas reales. La teoría de la probabilidad proporciona un marco para modelar y razonar bajo incertidumbre. Los modelos gráficos probabilísticos combinan la teoría estadística y la teoría de grafos con el objetivo de proporcionar una herramienta con la que trabajar bajo incertidumbre. En particular, nos centraremos en las redes bayesianas, el modelo más utilizado dentro de los modelos gráficos probabilísticos. En esta tesis hemos diseñado nuevos métodos para aprender redes bayesianas, inspirados por y aplicados al problema del modelado y análisis de datos morfológicos de neuronas. La morfología de una neurona puede ser cuantificada usando una serie de medidas, por ejemplo, la longitud de las dendritas y el axón, el número de bifurcaciones, la dirección de las dendritas y el axón, etc. Estas medidas pueden ser modeladas como datos continuos o discretos. A su vez, los datos continuos pueden ser lineales (por ejemplo, la longitud o la anchura de una dendrita) o direccionales (por ejemplo, la dirección del axón). Estos datos pueden llegar a seguir distribuciones de probabilidad muy complejas y pueden no ajustarse a ninguna distribución paramétrica conocida. El modelado de este tipo de problemas con redes bayesianas híbridas incluyendo variables discretas, lineales y direccionales presenta una serie de retos en relación al aprendizaje a partir de datos, la inferencia, etc. En esta tesis se propone un método para modelar y simular árboles dendríticos basales de neuronas piramidales usando redes bayesianas para capturar las interacciones entre las variables del problema. Para ello, se mide un amplio conjunto de variables de las dendritas y se aplica un algoritmo de aprendizaje con el que se aprende la estructura y se estiman los parámetros de las distribuciones de probabilidad que constituyen las redes bayesianas. Después, se usa un algoritmo de simulación para construir dendritas virtuales mediante el muestreo de valores de las redes bayesianas. Finalmente, se lleva a cabo una profunda evaluaci ón para verificar la capacidad del modelo a la hora de generar dendritas realistas. En esta primera aproximación, las variables fueron discretizadas para poder aprender y muestrear las redes bayesianas. A continuación, se aborda el problema del aprendizaje de redes bayesianas con diferentes tipos de variables. Las mixturas de polinomios constituyen un método para representar densidades de probabilidad en redes bayesianas híbridas. Presentamos un método para aprender aproximaciones de densidades unidimensionales, multidimensionales y condicionales a partir de datos utilizando mixturas de polinomios. El método se basa en interpolación con splines, que aproxima una densidad como una combinación lineal de splines. Los algoritmos propuestos se evalúan utilizando bases de datos artificiales. Además, las mixturas de polinomios son utilizadas como un método no paramétrico de estimación de densidades para clasificadores basados en redes bayesianas. Después, se estudia el problema de incluir información direccional en redes bayesianas. Este tipo de datos presenta una serie de características especiales que impiden el uso de las técnicas estadísticas clásicas. Por ello, para manejar este tipo de información se deben usar estadísticos y distribuciones de probabilidad específicos, como la distribución univariante von Mises y la distribución multivariante von Mises–Fisher. En concreto, en esta tesis extendemos el clasificador naive Bayes al caso en el que las distribuciones de probabilidad condicionada de las variables predictoras dada la clase siguen alguna de estas distribuciones. Se estudia el caso base, en el que sólo se utilizan variables direccionales, y el caso híbrido, en el que variables discretas, lineales y direccionales aparecen mezcladas. También se estudian los clasificadores desde un punto de vista teórico, derivando sus funciones de decisión y las superficies de decisión asociadas. El comportamiento de los clasificadores se ilustra utilizando bases de datos artificiales. Además, los clasificadores son evaluados empíricamente utilizando bases de datos reales. También se estudia el problema de la clasificación de interneuronas. Desarrollamos una aplicación web que permite a un grupo de expertos clasificar un conjunto de neuronas de acuerdo a sus características morfológicas más destacadas. Se utilizan medidas de concordancia para analizar el consenso entre los expertos a la hora de clasificar las neuronas. Se investiga la idoneidad de los términos anatómicos y de los tipos neuronales utilizados frecuentemente en la literatura a través del análisis de redes bayesianas y la aplicación de algoritmos de clustering. Además, se aplican técnicas de aprendizaje supervisado con el objetivo de clasificar de forma automática las interneuronas a partir de sus valores morfológicos. A continuación, se presenta una metodología para construir un modelo que captura las opiniones de todos los expertos. Primero, se genera una red bayesiana para cada experto y se propone un algoritmo para agrupar las redes bayesianas que se corresponden con expertos con comportamientos similares. Después, se induce una red bayesiana que modela la opinión de cada grupo de expertos. Por último, se construye una multired bayesiana que modela las opiniones del conjunto completo de expertos. El análisis del modelo consensuado permite identificar diferentes comportamientos entre los expertos a la hora de clasificar las neuronas. Además, permite extraer un conjunto de características morfológicas relevantes para cada uno de los tipos neuronales mediante inferencia con la multired bayesiana. Estos descubrimientos se utilizan para validar el modelo y constituyen información relevante acerca de la morfología neuronal. Por último, se estudia un problema de clasificación en el que la etiqueta de clase de los datos de entrenamiento es incierta. En cambio, disponemos de un conjunto de etiquetas para cada instancia. Este problema está inspirado en el problema de la clasificación de neuronas, en el que un grupo de expertos proporciona una etiqueta de clase para cada instancia de manera individual. Se propone un método para aprender redes bayesianas utilizando vectores de cuentas, que representan el número de expertos que seleccionan cada etiqueta de clase para cada instancia. Estas redes bayesianas se evalúan utilizando bases de datos artificiales de problemas de aprendizaje supervisado.

Decision boundary for discrete Bayesian network classifiers

Bayesian network classifiers are a powerful machine learning tool. In order to evaluate the expressive power of these models, we compute families of polynomials that sign-represent decision functions induced by Bayesian network classifiers. We prove that those families are linear combinations of products of Lagrange basis polynomials. In absence of V-structures in the predictor sub-graph, we are also able to prove that this family of polynomials does in- deed characterize the specific classifier considered. We then use this representation to bound the number of decision functions representable by Bayesian network classifiers with a given structure and we compare these bounds to the ones obtained using Vapnik-Chervonenkis dimension.