778 resultados para self-learning algorithm


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Electronics Letters Vol.38, nº 19

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Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Ciência e Sistemas de Informação Geográfica

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Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Ciência e Sistemas de Informação Geográfica

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Proceedings of IEEE, ISCAS 2003, Vol.I, pp. 877-880

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Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

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Dissertação para obtenção do Grau de Doutor em Estatística e Gestão do Risco

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Search is now going beyond looking for factual information, and people wish to search for the opinions of others to help them in their own decision-making. Sentiment expressions or opinion expressions are used by users to express their opinion and embody important pieces of information, particularly in online commerce. The main problem that the present dissertation addresses is how to model text to find meaningful words that express a sentiment. In this context, I investigate the viability of automatically generating a sentiment lexicon for opinion retrieval and sentiment classification applications. For this research objective we propose to capture sentiment words that are derived from online users’ reviews. In this approach, we tackle a major challenge in sentiment analysis which is the detection of words that express subjective preference and domain-specific sentiment words such as jargon. To this aim we present a fully generative method that automatically learns a domain-specific lexicon and is fully independent of external sources. Sentiment lexicons can be applied in a broad set of applications, however popular recommendation algorithms have somehow been disconnected from sentiment analysis. Therefore, we present a study that explores the viability of applying sentiment analysis techniques to infer ratings in a recommendation algorithm. Furthermore, entities’ reputation is intrinsically associated with sentiment words that have a positive or negative relation with those entities. Hence, is provided a study that observes the viability of using a domain-specific lexicon to compute entities reputation. Finally, a recommendation system algorithm is improved with the use of sentiment-based ratings and entities reputation.

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The present study investigates peer to peer oral interaction in two task based language teaching classrooms, one of which was a self-declared cohesive group, and the other a self- declared less cohesive group, both at B1 level. It studies how learners talk cohesion into being and considers how this talk leads to learning opportunities in these groups. The study was classroom-based and was carried out over the period of an academic year. Research was conducted in the classrooms and the tasks were part of regular class work. The research was framed within a sociocognitive perspective of second language learning and data came from a number of sources, namely questionnaires, interviews and audio recorded talk of dyads, triads and groups of four students completing a total of eight oral tasks. These audio recordings were transcribed and analysed qualitatively for interactions which encouraged a positive social dimension and behaviours which led to learning opportunities, using conversation analysis. In addition, recordings were analysed quantitatively for learning opportunities and quantity and quality of language produced. Results show that learners in both classes exhibited multiple behaviours in interaction which could promote a positive social dimension, although behaviours which could discourage positive affect amongst group members were also found. Analysis of interactions also revealed the many ways in which learners in both the cohesive and less cohesive class created learning opportunities. Further qualitative analysis of these interactions showed that a number of factors including how learners approach a task, the decisions they make at zones of interactional transition and the affective relationship between participants influence the amount of learning opportunities created, as well as the quality and quantity of language produced. The main conclusion of the study is that it is not the cohesive nature of the group as a whole but the nature of the relationship between the individual members of the small group completing the task which influences the effectiveness of oral interaction for learning.This study contributes to our understanding of the way in which learners individualise the learning space and highlights the situated nature of language learning. It shows how individuals interact with each other and the task, and how talk in interaction changes moment-by-moment as learners react to the ‘here and now’ of the classroom environment.

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Forming suitable learning groups is one of the factors that determine the efficiency of collaborative learning activities. However, only a few studies were carried out to address this problem in the mobile learning environments. In this paper, we propose a new approach for an automatic, customized, and dynamic group formation in Mobile Computer Supported Collaborative Learning (MCSCL) contexts. The proposed solution is based on the combination of three types of grouping criteria: learner’s personal characteristics, learner’s behaviours, and context information. The instructors can freely select the type, the number, and the weight of grouping criteria, together with other settings such as the number, the size, and the type of learning groups (homogeneous or heterogeneous). Apart from a grouping mechanism, the proposed approach represents a flexible tool to control each learner, and to manage the learning processes from the beginning to the end of collaborative learning activities. In order to evaluate the quality of the implemented group formation algorithm, we compare its Average Intra-cluster Distance (AID) with the one of a random group formation method. The results show a higher effectiveness of the proposed algorithm in forming homogenous and heterogeneous groups compared to the random method.

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Tese de Doutoramento em Tecnologias e Sistemas de Informação

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Many of our everyday tasks require the control of the serial order and the timing of component actions. Using the dynamic neural field (DNF) framework, we address the learning of representations that support the performance of precisely time action sequences. In continuation of previous modeling work and robotics implementations, we ask specifically the question how feedback about executed actions might be used by the learning system to fine tune a joint memory representation of the ordinal and the temporal structure which has been initially acquired by observation. The perceptual memory is represented by a self-stabilized, multi-bump activity pattern of neurons encoding instances of a sensory event (e.g., color, position or pitch) which guides sequence learning. The strength of the population representation of each event is a function of elapsed time since sequence onset. We propose and test in simulations a simple learning rule that detects a mismatch between the expected and realized timing of events and adapts the activation strengths in order to compensate for the movement time needed to achieve the desired effect. The simulation results show that the effector-specific memory representation can be robustly recalled. We discuss the impact of the fast, activation-based learning that the DNF framework provides for robotics applications.

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For a given self-map f of M, a closed smooth connected and simply-connected manifold of dimension m ≥ 4, we provide an algorithm for estimating the values of the topological invariant Dm r [f], which equals the minimal number of r-periodic points in the smooth homotopy class of f. Our results are based on the combinatorial scheme for computing Dm r [f] introduced by G. Graff and J. Jezierski [J. Fixed Point Theory Appl. 13 (2013), 63–84]. An open-source implementation of the algorithm programmed in C++ is publicly available at http://www.pawelpilarczyk.com/combtop/.

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Este plan de trabajos es continuidad de una línea de investigación centrada en evaluar los mecanismos responsables de la adquisición, expresión y persistencia de experiencias con el etanol. A partir de ello, indagar acerca de los efectos de esta experiencia sobre comportamientos de búsqueda y autoadministración de etanol en neonatos e infantes de rata. Se pretende analizar la participación del sistema opiáceo en los mecanismos implicados en una memoria fetal y/o infantil, generada como consecuencia de la exposición etílica. En una primera etapa, nos proponemos establecer de qué manera experiencias prenatales con la droga modulan el patrón de auto-administración de alcohol y otros reforzadores, como sacarosa. En este primer bloque de experimentos realizaremos manipulaciones fetales para determinar con mayor grado de especificidad la posible acción del sistema opiáceo en los mecanismos de adquisición de una memoria etílica prenatal. Se realizarán administraciones de etanol y el antagonista opiáceo, directamente a nivel fetal, y se evaluará esta experiencia en un paradigma de condicionamiento neonatal positivo, mediado por la droga. De acuerdo a la evidencia previa, esperamos que la exposición prenatal con la droga facilite la expresión de conductas de consumo y búsqueda del etanol o hacia las claves que señalizan al psicotrópico, tanto durante la infancia como en el neonato. A su vez, cuando la droga es presentada bajo los efectos de un antagonista opiáceo esperamos que estas conductas muestren un perfil similar a las desplegadas por sujetos controles. El segundo bloque de experimentos ha sido ideado con el objeto de indagar acerca de la posible participación del sistema opiáceo en la modulación de los aspectos reforzantes de la droga, a través de un esquema de auto-administración etílica infantil. Se utilizará un paradigma de condicionamiento instrumental adaptado para ratas infantes que consta de dos instancias, una de adquisición de la conducta instrumental (DPs 14-17) en la cual los animales reciben un pulso de refuerzo, como consecuencia de la ejecución de la conducta operante. En una segunda fase se analiza el patrón de búsqueda del reforzador ya que se registra la respuesta instrumental, sin que ocurra el refuerzo por la misma. Para analizar la participación del sistema opiáceo, durante la fase de adquisición de la conducta operante (DPs 16 y 17) los animales serán re-expuestos a mínimas cantidades del reforzador, bajo los efectos de un antagonista opiáceo, momentos previos al ensayo instrumental correspondiente para cada uno de estos días (Exp. 3). Esperamos que el bloqueo del sistema opiáceo, durante esta re-exposición al etanol, sea suficiente para disminuir el patrón de respuesta instrumental hacia el refuerzo etílico. Un último experimento incorporará un tercer evento de re-exposición al etanol -bajo los efectos del antagonista- previo al ensayo de extinción de la conducta instrumental (DP 18). Este nuevo evento tiene por objeto analizar la participación de este sistema neurobiológico en los mecanismos de búsqueda de etanol. Si el sistema opiáceo participa en la modulación de patrones tanto de búsqueda como consumatorios del reforzamiento por etanol, se espera que la re-exposición a la droga bajo los efectos del antagonista, inhiba estas respuestas tanto durante la sesión de adquisición, como de extinción de la conducta operante. Este proyecto intenta profundizar en el conocimiento de los mecanismos que regulan reconocimiento, aceptación, búsqueda y consumo de etanol, como consecuencia de experiencias tempranas con la droga. A su vez, es importante identificar y estudiar los sistemas neurobiológicos involucrados en estos mecanismos. Es por ello que se intenta determinar el rol que ejerce el sistema opiáceo en la adquisición de estas experiencias etílicas a nivel fetal e infantil, que se conoce promueven la búsqueda y el consumo de la droga. Our work is directed to analyze the involvement of the opioid system in the generation of pre- and early postnatal ethanol-related memories. As a first step, maternal manipulations with ethanol will be done. Infants will be evaluated in a paradigm of infantile self-administration of different reinforcers (ethanol, sucrose or water), employing a model of operant conditioning adapted to infant rats. A second experiment will be conducted in order to analyze if a central administration of ethanol, directly to the fetus, modifies subsequent patterns of neonatal conditioned responses to an artificial nipple, mediated by ethanol reinforcing effects. Fetal presentation of ethanol will be accompanied with the injection of an opioid antagonist in order to analyze the involvement of this system in acquisition processes of a fetal ethanol-mediated memory. A second set of studies will be conducted to analyze appetitive and consummatory behaviors in an infant model of ethanol self-administration. Involvement of opioid system in the acquisition or expression of this experience will be also inquired. Infant rats (PDs14-17) have to display a target behavior (nose-poke) to gain access to 5% sucrose or 3.75% ethanol. On PD18 an extinction session will be included. At PDs16-17, 6-hr before training, pups will be re-exposed to ethanol under opioid antagonism effects (naloxone). In a follow up experiment, a re-exposure trial will be included at PD18. Prior extinction, pups will receive naloxone and will be re-exposed to ethanol. We aim to observe if opioid system is modulating etha¬nol reinforcing effects, in terms of both appetitive and consummatory behaviors.

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Agents have two forecasting models, one consistent with the unique rational expectations equilibrium, another that assumes a time-varying parameter structure. When agents use Bayesian updating to choose between models in a self-referential system, we find that learning dynamics lead to selection of one of the two models. However, there are parameter regions for which the non-rational forecasting model is selected in the long-run. A key structural parameter governing outcomes measures the degree of expectations feedback in Muth's model of price determination.

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We study the impact of anticipated fiscal policy changes in a Ramsey economy where agents form long-horizon expectations using adaptive learning. We extend the existing framework by introducing distortionary taxes as well as elastic labour supply, which makes agents. decisions non-predetermined but more realistic. We detect that the dynamic responses to anticipated tax changes under learning have oscillatory behaviour that can be interpreted as self-fulfilling waves of optimism and pessimism emerging from systematic forecast errors. Moreover, we demonstrate that these waves can have important implications for the welfare consequences of .scal reforms. (JEL: E32, E62, D84)