WorldCat Identities

Cerquides Bueno, Jesús

Overview
Works: 22 works in 37 publications in 3 languages and 55 library holdings
Roles: dgs, Author, Other
Publication Timeline
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Most widely held works by Jesús Cerquides Bueno
Learning inhomogeneous parsimonious Markov models with application to DNA sequence analysis by Ralf Eggeling( )

2 editions published in 2014 in English and held by 18 WorldCat member libraries worldwide

Bioinformatik; maschinelles Lernen; probabilistische graphische Modelle; parsimonische Kontextbäume; Modellselektion; verborgene Variablen; Transkriptionsfaktorbindestellen; de-novo Motivsuche; statistische Abhängigkeiten
Improved algorithms for learning Bayesian network classifiers by Jesús Cerquides( Book )

3 editions published in 2005 in English and held by 6 WorldCat member libraries worldwide

This thesis applies objective Bayesian probability theory techniques to improve Bayesian network classifiers. The main contributions are: {u2500} A parallelizable distance based discretization method that allows to extend discrete classifiers to non-discrete domains. {u2500} The concepts of qualitative influences and synergies, which allow to improve understandability of Bayesian network classifiers. {u2500} The first order and second order qualitative Bayesian classifiers, which are classifiers based on qualitative influences and synergies, with easily understandable results and with a reasonable accuracy. {u2500} INDIFFERENTNB, an improved version of the naive Bayes classifier based on the naive Bayes model, naive distributions, Bayesian model averaging and the principle of indifference that improves the quality of the probabilities of the maximum likelihood naive Bayes classifier. {u2500} STAN+BMA, a classification algorithm based on applying empirical local Bayesian model averaging to the stan (softened TAN) classifier and which improves its accuracy. {u2500} TBMATAN, a classification algorithm based on the computability of the averaging of TAN models under decomposable distributions over TANs and the principle of indifference that improves stan accuracy. {u2500} SSTBMATAN, an efficient approximation to TBMATAN which provides also improved accuracy. {u2500} MAPTAN, a classification algorihtm that computes the maximum a posteriori TAN model and improves STAN accuracy. {u2500} MAPTAN+BMA, a classification algorithm that computes the k maximum a posteriori TAN models and their relative weights efficiently and improves STAN accuracy and STAN+BMA accuracy and learning time. These results show that the joint application of Bayesian model averaging and a careful selection of the prior probability distribution over the set of models, following objective Bayesian techniques whenever it is possible, can provide significant improvements to classification algorithms
Enlaces de PNL con PERL y RUBY by Verònica Rosalench Ferrer( )

2 editions published in 2005 in Spanish and held by 2 WorldCat member libraries worldwide

Implementació d'una interfície per una aplicació de classificació i anàlisi automàtic d'espectogrames de masses per al diagnòstic i la investigació clínica by Martí Bernardo Faura( )

2 editions published in 2007 in Catalan and held by 2 WorldCat member libraries worldwide

Disseny i implementació d'una GUI per un simulador per a xarxes de sensors by Marc Cano Canal( )

2 editions published in 2008 in Spanish and Catalan and held by 2 WorldCat member libraries worldwide

Incremental active learning of sensorimotor models in developmental robotics by Arturo Ribes Sanz( Book )

2 editions published in 2015 in English and held by 2 WorldCat member libraries worldwide

La rápida evolución de la robótica esta promoviendo que emerjan nuevos campos relacionados con la robótica. Inspirándose en ideas provinientes de la psicología del desarrollo, la robótica del desarrollo es un nuevo campo que pretende proveer a los robots de capacidades que les permiten aprender de una manera abierta durante toda su vida. Hay situaciones donde los ingenieros o los diseñadores no pueden prever todos los posibles problemas que un robot pueda encontrar. Tal como el número de tareas que un robot debe hacer crece, este problema se vuelve más evidente, y las soluciones de ingenería tradicionales pueden no ser completamente factibles. En tal caso, la robótica del desarrollo proporciona una serie de principios y directrices para construir robots que tienen las herramientas cognitivas adecuadas a fin de adquirir el conocimiento necesario. Auto-exploración, aprendizaje incremental, scaffolding social e imitación. Todas son herramientas que contribuyen a construir robots con un alto grado de autonomía. Mediante la auto-exploración internamente motivada, un robot descubre lo que su cuerpo es capaz de hacer. Las técnicas de aprendizaje incremental permite que un robot tenga conocimiento listo al instante, a partir de construir estructuras cognitivas encima de otras más viejas. El scaffolding o andamiaje social y las capacidades de imitación permiten aprovechar lo que los humanos -- u otros robots -- ya saben. De esta manera, los robots tienen metas que perseguir y aportan, o bien un uso final para las habilidades aprendidas, o bien ejemplos de cómo lograr una determinada tarea. Esta tesis presenta un estudio de una serie de técnicas, las cuales ejemplifican cómo algunos de esos principios, aplicados a robots reales, funcionan juntos, permitiendo al robot aprender autónomamente a ejecutar una serie de tareas. También mostramos cómo el robot, aprovechándose de técnicas de aprendizaje activo e incremental, es capaz de decidir la mejor manera de explorar su entorno a fin de adquirir el conocimiento que mejor le ayuda a lograr sus objetivos. Ésto, añadido al descubrimiento autónomo de las limitaciones de su propio cuerpo, disminuye la cantidad de conocimiento especifico del dominio que es necesario poner en el diseño del sistema de aprendizaje. Primeramente, presentamos un algoritmo de aprendizaje incremental para Modelos de Mixtura de Gaussianas aplicado al problema de aprendizaje sensorimotor. Implementado en un robot móvil, el objetivo es adquirir un modelo que es capaz de realizar predicciones sobre los estados sensoriales futuros. Este modelo predictivo es reutilizado como substrato representacional, el cual sirve para categorizar y anticipar situaciones tales como la colisión contra un objeto. Después de un periodo extendido de aprendizaje, y habiendo encontrado situaciones diferentes, observamos que los modelos adquiridos se terminan siendo bastante grandes. Sin embargo, nos dimos cuenta que, en un momento dado, solo una pequeña porcion del mismo es utilizada. Además, estas areas son utilizadas consistentemente por un periodo relativamente largo de tiempo. Presentamos una extensión para el algoritmo de Regresión basado en Mixturas de Gaussianas, el cual aprovecha este hecho a fin de reducir el coste computacional de la inferencia. Las técnicas aquí presentadas fueron también aplicadas en un problema diferente y más commplejo: la imitación de una secuencia de notas musicales proporcionadas por un humano. Estas son producidas por un objeto musical virtual que es utilizado por un robot humanoide. El robot no solo aprende a utilizar este objeto, sino que también aprende sobre las limitaciones de su propio cuerpo. Ésto le permite entender mejor qué puede hacer y cómo puede hacerlo, subrayando la importancia de la influencia que el hecho de tener cuerpo tiene en la interacción del robot con su entorno y el tipo de estructuras cognitivas que se forman como consecuencia de este tipo de interacción
Programación de una tienda virtual en grails by Gabriel Bermúdez Merino( )

2 editions published in 2008 in Spanish and held by 2 WorldCat member libraries worldwide

Implementación de una aplicación web utilizando frameworks J2EE : Struts2, Spring, Hibernate by Ángel Gómez García( )

2 editions published in 2008 in Catalan and held by 2 WorldCat member libraries worldwide

Approximate algorithms for decentralized supply chain formation by Toni Penya-Alba( Book )

2 editions published between 2014 and 2015 in English and held by 2 WorldCat member libraries worldwide

Supply chain formation involves determining the participants and the exchange of goods within a production network. Today's companies operate autonomously, making local decisions, and coordinating with other companies to buy and sell goods along their supply chains. Decentralized decision making is well suited to this scenario since it better preserves the privacy of the participants, offers better scalability on large-scale scenarios, and is more resilient to failure. Moreover, decentralized supply chain formation can be tackled either by means of peer-to-peer communication between supply chain participants or by introducing local markets that mediate the trading of goods. Unfortunately, current approaches to decentralized supply chain formation, both in the peer- to-peer and the mediated scenario, are unable to provide computationally and economically efficient solutions to the supply chain formation problem. The main goal of this dissertation is to provide computationally and eco- nomically efficient methods for decentralized supply chain formation both in the peer-to-peer and the mediated scenario. This is achieved by means of two optimized max-sum based methods for supply chain formation. On the one hand, we contribute to peer-to-peer supply chain formation via the so-called Reduced Binarized Loopy Belief Propagation (rb-lbp) algorithm. The rb-lbp algorithm is run by a multi-agent system in which each of the participants in the supply chain is represented by a computational agent. Moreover, rb-lbp's message computation mechanisms allow the efficient computation of max-sum messages. This results in an algorithm that is able to find solutions to the supply chain formation problem of higher value than the state of the art while reducing the memory, bandwidth and computational resources required by several orders of magnitude. On the other hand, we contribute to mediated supply chain formation via the so-called CHaining Agents IN Mediated Environments (chainme) algorithm. The chainme algorithm is run by a multi-agent system in which each of the participants and each of the goods in the supply chain is represented by a computational agent. In chainme participant agents communicate exclusively with the agents representing the goods who act as mediators. Likewise rb-lbp, chainme is also endowed with a message computation mechanism for the efficient computation of max-sum messages. This results in an algorithm that is able to find economically efficient solutions while requiring a fraction of the computa- tional resources needed by the state-of-the-art methods for both peer-to-peer and mediated supply chain formation. Finally, the design and implementation of both of our contributions to decentralized supply chain formation follow the same methodology. That is, we first map the problem at hand into a local term graph over which max-sum can operate. Then, we assign each max-sum local term to a computational agent. Last, we derive computationally efficient expressions to assess the max-sum messages exchanged between these agents. Although our methodology proved to be valid for the design of SCF algorithms, its generality makes it appear as a promising candidate for other multi-agent coordination problems
Eines de videojockey GNU/linux Imagegrid per Puredata by Sergi Lario Loyo( )

2 editions published in 2007 in Catalan and held by 2 WorldCat member libraries worldwide

Enlaces de PNL con PERL y RUBY (II) by Jaime Giménez Espejo( )

2 editions published in 2005 in Spanish and Catalan and held by 2 WorldCat member libraries worldwide

Estudi de l'aprenentatge en jocs iterats dins el marc de la teoria de jocs by Esteban Fernández González( )

2 editions published in 2006 in Catalan and held by 2 WorldCat member libraries worldwide

Implementación de algoritmos de aprendizaje de redes bayesianas by David Morales Mojica( Book )

2 editions published in 2005 in Spanish and held by 2 WorldCat member libraries worldwide

Cespatech portal de aplicaciones by Disraeli Mateo Rocha( )

2 editions published in 2008 in Spanish and held by 1 WorldCat member library worldwide

Improving Bayesian network classifiers by Jesús Cerquides Bueno( )

1 edition published in 2003 in English and held by 1 WorldCat member library worldwide

Exploring the topical structure of short text through probability models : from tasks to fundamentals by Joan Capdevila Pujol( )

1 edition published in 2020 in English and held by 1 WorldCat member library worldwide

Recent technological advances have radically changed the way we communicate. Today'scommunication has become ubiquitous and it has fostered the need for information that is easier to create, spread and consume. As a consequence, we have experienced the shortening of text messages in mediums ranging from electronic mailing, instant messaging to microblogging. Moreover, the ubiquity and fast-paced nature of these mediums have promoted their use for unthinkable tasks. For instance, reporting real-world events was classically carried out by news reporters, but, nowadays, most interesting events are first disclosed on social networks like Twitter by eyewitness through short text messages. As a result, the exploitation of the thematic content in short text has captured the interest of both research and industry.Topic models are a type of probability models that have traditionally been used to explore this thematic content, a.k.a. topics, in regular text. Most popular topic models fall into the sub-class of LVMs (Latent Variable Models), which include several latent variables at the corpus, document and word levels to summarise the topics at each level. However, classical LVM-based topic models struggle to learn semantically meaningful topics in short text because the lack of co-occurring words within a document hampers the estimation of the local latent variables at the document level. To overcome this limitation, pooling and hierarchical Bayesian strategies that leverage on contextual information have been essential to improve the quality of topics in short text.In this thesis, we study the problem of learning semantically meaningful and predictive representations of text in two distinct phases:• In the first phase, Part I, we investigate the use of LVM-based topic models for the specific task of event detection in Twitter. In this situation, the use of contextual information to pool tweets together comes naturally. Thus, we first extend an existing clustering algorithm for event detection to use the topics learned from pooled tweets. Then, we propose a probability model that integrates topic modelling and clustering to enable the flow of information between both components.• In the second phase, Part II and Part III, we challenge the use of local latent variables in LVMs,specially when the context of short messages is not available. First of all, we study the evaluation of thegeneralization capabilities of LVMs like PFA (Poisson Factor Analysis) and propose unbiased estimation methods to approximate it. With the most accurate method, we compare the generalization of chordal models without latent variables to that of PFA topic models in short and regular text collections.In summary, we demonstrate that by integrating clustering and topic modelling, the performance of event detection techniques in Twitter is improved due to the interaction between both components. Moreover, we develop several unbiased likelihood estimation methods for assessing the generalization of PFA and we empirically validate their accuracy in different document collections. Finally, we show that we can learn chordal models without latent variables in text through Chordalysis, and that they can be a competitive alternative to classical topic models, specially in short text
EGuara creación de una red P2P by Raúl Roca Cánovas( )

1 edition published in 2006 in Spanish and held by 1 WorldCat member library worldwide

Desenvolupament d'una aplicació web per a l'administració de sol·licituds d'informació i inscripcions a convocatòries by Jordi Petchamé Sala( )

1 edition published in 2007 in Catalan and held by 1 WorldCat member library worldwide

Bayesian Gaussian network classifiers for mass spectra classification by Víctor Manuel Bellón Molina( )

1 edition published in 2013 in English and held by 1 WorldCat member library worldwide

Scaling DCOP algorithms for cooperative multi-agent coordination by Marc Pujol-González( )

1 edition published in 2014 in English and held by 1 WorldCat member library worldwide

 
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Alternative Names
Cerquides, Jesús (Cerquides Bueno)

Languages
English (13)

Spanish (11)

Catalan (11)