670 resultados para New Learning


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Dissertação de mestrado integrado em Psicologia

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This paper investigates the role of learning by private agents and the central bank (two-sided learning) in a New Keynesian framework in which both sides of the economy have asymmetric and imperfect knowledge about the true data generating process. We assume that all agents employ the data that they observe (which may be distinct for different sets of agents) to form beliefs about unknown aspects of the true model of the economy, use their beliefs to decide on actions, and revise these beliefs through a statistical learning algorithm as new information becomes available. We study the short-run dynamics of our model and derive its policy recommendations, particularly with respect to central bank communications. We demonstrate that two-sided learning can generate substantial increases in volatility and persistence, and alter the behavior of the variables in the model in a signifficant way. Our simulations do not converge to a symmetric rational expectations equilibrium and we highlight one source that invalidates the convergence results of Marcet and Sargent (1989). Finally, we identify a novel aspect of central bank communication in models of learning: communication can be harmful if the central bank's model is substantially mis-specified

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This paper investigates the role of learning by private agents and the central bank(two-sided learning) in a New Keynesian framework in which both sides of the economyhave asymmetric and imperfect knowledge about the true data generating process. Weassume that all agents employ the data that they observe (which may be distinct fordifferent sets of agents) to form beliefs about unknown aspects of the true model ofthe economy, use their beliefs to decide on actions, and revise these beliefs througha statistical learning algorithm as new information becomes available. We study theshort-run dynamics of our model and derive its policy recommendations, particularlywith respect to central bank communications. We demonstrate that two-sided learningcan generate substantial increases in volatility and persistence, and alter the behaviorof the variables in the model in a significant way. Our simulations do not convergeto a symmetric rational expectations equilibrium and we highlight one source thatinvalidates the convergence results of Marcet and Sargent (1989). Finally, we identifya novel aspect of central bank communication in models of learning: communicationcan be harmful if the central bank's model is substantially mis-specified.

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Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.

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The fact that individuals learn can change the relationship between genotype and phenotype in the population, and thus affect the evolutionary response to selection. Here we ask how male ability to learn from female response affects the evolution of a novel male behavioral courtship trait under pre-existing female preference (sensory drive). We assume a courtship trait which has both a genetic and a learned component, and a two-level female response to males. With individual-based simulations we show that, under this scenario, learning generally increases the strength of selection on the genetic component of the courtship trait, at least when the population genetic mean is still low. As a consequence, learning not only accelerates the evolution of the courtship trait, but also enables it when the trait is costly, which in the absence of learning results in an adaptive valley. Furthermore, learning can enable the evolution of the novel trait in the face of gene flow mediated by immigration of males that show superior attractiveness to females based on another, non-heritable trait. However, rather than increasing monotonically with the speed of learning, the effect of learning on evolution is maximized at intermediate learning rates. This model shows that, at least under some scenarios, the ability to learn can drive the evolution of mating behaviors through a process equivalent to Waddington's genetic assimilation.

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Este trabajo persigue dos objetivos: el primero es analizar el uso de las TIC en un grupo de estudiantes de segundo curso de Magisterio de la Universidad de Girona; el segundo es analizar los documentos normativos legales que establecen el currículum de educación primaria en Cataluña para observar qué tipo de papel juegan las TIC en las nuevas programaciones educativas. La primera parte se ha llevado a cabo mediante una encuesta, cuyos resultados permiten observar tres aspectos distintos: el primero, que una parte considerable del grupo considera las TIC más como un complemento para el aprendizaje que como una forma de aprendizaje; el segundo, que a pesar de hacer un uso considerable de las TIC, el conocimiento que tienen de ellas es muy básico y utilizan aplicaciones muy genéricas; y el tercero es que una parte de sus propuestas didácticas para el uso de las TIC son propuestas tradicionales simplemente adaptadas a un nuevo instrumento, sin buscar realmente la innovación que puede suponer la incorporación de las TIC. En la segunda parte del artículo, a partir del análisis e interpretación de los documentos legales que establecen el currículum de Educación Primaria, se observa que en un mismo documento conviven aserciones sobre las TIC como complemento al aprendizaje de contenidos con otras formulaciones que consideran las TIC como constructoras de conocimiento. A partir del perfil de los estudiantes y del estado de los documentos legales, al final del artículo se hacen propuestas para formar al futuro maestro teniendo en cuenta las TIC como herramientas básicas de conocimiento

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The legislative reforms in university matters driven in recent years, beyond the provoked controversies, offer to universities the possibility to develop a new model in line with the European environment, focusing on quality aims and adapting to the socioeconomic current challenges. A new educational model centered on the student, on the formation of specific and transverse competitions, on the improvement of the employability and the access to the labor market, on the attraction and fixation of talent, is an indispensable condition for the effective social mobility and for the homogeneous development of a more responsible and sustainable socioeconomic and productive model

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The aim of the study is to expand networking between a packaging material manufacturer and retailers in order to develop products which appeal to brand owners and their customers. The in-built targets are to understand the retailer’s role in the value chain, clarify who makes packaging decision of private label products, and canvass the importance of sustainability. The present value chain of the packaging material manufacturer is reviewed first. It is assumed that sustainability could be a common interest, and The Consumer Goods Forum’s “A Global Language for Packaging and Sustainability” report is shortly discussed. The presentation of the most common packaging materials is based on a guide called “Packaging in the Sustainability Agenda: A Guide for Corporate Decision Makers”. The terms manufacturer’s brand and private label are defined. A retail value chain with emphasis on the role of customers as partners is introduced. The study area is the Nordic countries, and the information about Nordic retailers was provided first by desk research. The interviews were made in Finland, Sweden, Norway and Denmark. The study method is qualitative: the intention was to get initial insights, ideas and understandings. The results are compiled under the headings: sustainability, private labels, cooperation and packaging development. Also the reasons for good profitability of private labels are explained. Sustainability or responsibility is a key driver for innovation in the retail sector. Private labels have become brands. The ways of cooperation between a packaging material manufacturer and a retailer could be education and training. Packaging development is of great interest to retailers and they are willing to contribute.

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A class of twenty-two grade one children was tested to determine their reading levels using the Stanford Diagnostic Reading Achievement Test. Based on these results and teacher input the students were paired according to reading ability. The students ages ranged from six years four months to seven years four months at the commencement of the study. Eleven children were assigned to the language experience group and their partners became the text group. Each member of the language experience group generated a list of eight to be learned words. The treatment consisted of exposing the student to a given word three times per session for ten sessions, over a period of five days. The dependent variables consisted of word identification speed, word identification accuracy, and word recognition accuracy. Each member of the text group followed the same procedure using his/her partner's list of words. Upon completion of this training, the entire process was repeated with members of the text group from the first part becoming members of the language experience group and vice versa. The results suggest that generally speaking language experience words are identified faster than text words but that there is no difference in the rate at which these words are learned. Language experience words may be identified faster because the auditory-semantic information is more readily available in them than in text words. The rate of learning in both types of words, however, may be dictated by the orthography of the to be learned word.

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EI Salvador presents an unfortunate history that includes a military regime and a civil war that together created a legacy of violence in which the country still struggle nowadays. Salud Escolar Integral (SEI) was created in 2005 as a program to combat youth violence throughout the re-formulation of physical education (PE) classes in public schools, promoting life skills learning that supports the resolution of conflicts with nonviolent ways. In 2007, SEI supported the creation of a physical e~ucation teacher education (PETE) degree at the Universidad Pedag6gica de EI Salvador (UPES), having the goal to assist pre-service teachers with a better understanding of humanistic principles. The present research analyzed if after attending all three years ofUPES PETE program, students presented high self-perception levels of competence and confidence related to attitude, skills and knowledge to teach PE within humanistic principles. Taking Personal and Social Responsibility (TPSR) was the theoretical framework used to analyze the development of humanistic principles. The study had a mixed-method longitudinal design that included questionnaires, reflection templates and interviews. In conclusion, although it is suggested that UPES should provide better support for the development of the teaching principles of empowering students and transfer learning, most of the humanistic principles were highly promoted by the program. At last, it is suggested that future research should track teachers' progress while teaching in schools, in order to analyze if the theory of promoting humanistic principles have also become a daily practice.

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UANL

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As AI has begun to reach out beyond its symbolic, objectivist roots into the embodied, experientialist realm, many projects are exploring different aspects of creating machines which interact with and respond to the world as humans do. Techniques for visual processing, object recognition, emotional response, gesture production and recognition, etc., are necessary components of a complete humanoid robot. However, most projects invariably concentrate on developing a few of these individual components, neglecting the issue of how all of these pieces would eventually fit together. The focus of the work in this dissertation is on creating a framework into which such specific competencies can be embedded, in a way that they can interact with each other and build layers of new functionality. To be of any practical value, such a framework must satisfy the real-world constraints of functioning in real-time with noisy sensors and actuators. The humanoid robot Cog provides an unapologetically adequate platform from which to take on such a challenge. This work makes three contributions to embodied AI. First, it offers a general-purpose architecture for developing behavior-based systems distributed over networks of PC's. Second, it provides a motor-control system that simulates several biological features which impact the development of motor behavior. Third, it develops a framework for a system which enables a robot to learn new behaviors via interacting with itself and the outside world. A few basic functional modules are built into this framework, enough to demonstrate the robot learning some very simple behaviors taught by a human trainer. A primary motivation for this project is the notion that it is practically impossible to build an "intelligent" machine unless it is designed partly to build itself. This work is a proof-of-concept of such an approach to integrating multiple perceptual and motor systems into a complete learning agent.