Página 1 dos resultados de 1857 itens digitais encontrados em 0.017 segundos

## A utilização do processo de avaliação on-line como apoio ao ensino presencial: desenvolvimento e análise junto ao laboratório virtual de estatística aplicada à administração - LaViE; Online evaluation process used as support to presencial teaching : development and analysis at the virtual laboratory of statistics applied to business management - LaViE

Marques, Érica Ferreira
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Relevância na Pesquisa
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## O ensino de estatística e a busca do equilíbrio entre os aspectos determinísticos e aleatórios da realidade; The teaching of statistics and the search for the equilibrium between deterministic and random aspects of reality

Ara, Amilton Braio
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Relevância na Pesquisa
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## Educação a distância na informação em saúde: o ensino do EPI INFO; Distance Learning in Health Information: Teaching Epi Info

Figueiredo, Marcia Aparecida
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
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## Método para implementação e acompanhamento de atividades a distância em disciplinas de Estatística: um estudo de caso; Method for distance activities introduction and attendance in statistics subjects: a case-study

Mantovani, Daielly Melina Nassif
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Relevância na Pesquisa
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## Análise da influência do estilo de aprendizagem e da atitude em disciplinas de estatística da FEARP; Analysis of influence of learning styles and attitude in statistics subjetcts at FEARP

Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Relevância na Pesquisa
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## Os saberes profissionais dos professores : a problematização das práticas pedagógicas em estatística mediadas pelas práticas colaborativas; Teacher's professional knowledge : the problematization of pedagogical practices in statistics mediated by collaborative practices

Maria Aparecida Vilela Mendonça Pinto Coelho
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Tese de Doutorado Formato: application/pdf
Relevância na Pesquisa
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O foco de interesse deste estudo é a aprendizagem profissional de um grupo de professores e seus objetivos são: investigar como professores de Matemática da Escola Básica que pertencem a um grupo do tipo colaborativo problematizaram suas concepções sobre Educação Estatística nas práticas de ensinar e aprender Estatística; e compreender como o movimento do grupo possibilitou a sistematização de saberes profissionais dos professores. A questão de investigação ficou formulada da seguinte maneira: Como o movimento do grupo mobilizou práticas de ensinar e aprender Estatística e possibilitou a sistematização de saberes profissionais dos professores? A pesquisa é de natureza qualitativa, buscando uma abordagem histórico-dialética, em uma vertente interpretativa, procurando apreender o caráter dinâmico, contraditório e histórico dos fenômenos educativos. O trabalho de pesquisa, que teve a duração de um ano, se orienta segundo duas vertentes: o Desenvolvimento Profissional de Professores e a Educação Estatística. Foi fundamentado nos aportes teóricos dos estudos histórico-culturais de Bakhtin e na perspectiva de Investigação como Postura de Cochran Smith e Lytle, que trabalham as relações entre conhecimento e prática e o papel do conhecimento gerado pelos professores em suas práticas pedagógicas. O grupo do tipo colaborativo...

## Come away with me: Statistics learning through collaborative work

César, Margarida
Tipo: Conferência ou Objeto de Conferência
Relevância na Pesquisa
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At a more technological and literate society statistics play a relevant role in order to allow people becoming critical and active citizens. Statistics is part of our daily life. Most media refer to statistical knowledge showing graphs, tables or means in order to sound scientifically supported and to be able to manipulate people’s opinion about what is going on in the world. Choosing the information one reads, analysing it, processing data and deciding different ways of presenting them are some of the competencies that we need to develop and mobilize. School practices play an essential role in the access pupils will have, or not, to these forms of literacy so deeply needed in a complex, changing and multicultural society. Nowadays most international and Portuguese policy documents refer that school should provide the means to develop each pupil’s competencies, namely the ones related to communication and to participant citizenship. Collaborative work is also suggested in many of them, namely related to statistical contents. Piaget and Vygotsky (Tryphon and Vonèche, 1996) underlined the role of communication in knowledge appropriation and in pupils’ performances. Social interaction, namely peer ones, played a main role in the process and could be seen as a facilitator when used within an innovative and coherent didactic contract (César...

## Attitudes towards statistics of graduate entry medical students: the role of prior learning experiences.

Hannigan, Ailish; Hegarty, Avril C; McGrath, Deirdre
Fonte: BioMed Central Publicador: BioMed Central
Tipo: info:eu-repo/semantics/article; all_ul_research; ul_published_reviewed
Português
Relevância na Pesquisa
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peer-reviewed; While statistics is increasingly taught as part of the medical curriculum, it can be an unpopular subject and feedback from students indicates that some find it more difficult than other subjects. Understanding attitudes towards statistics on entry to graduate entry medical programmes is particularly important, given that many students may have been exposed to quantitative courses in their previous degree and hence bring preconceptions of their ability and interest to their medical education programme. The aim of this study therefore is to explore, for the first time, attitudes towards statistics of graduate entry medical students from a variety of backgrounds and focus on understanding the role of prior learning experiences.; PUBLISHED; peer-reviewed

## Learning Games and Rademacher Observations Losses

Nock, Richard
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
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It has recently been shown that supervised learning with the popular logistic loss is equivalent to optimizing the exponential loss over sufficient statistics about the class: Rademacher observations (rados). We first show that this unexpected equivalence can actually be generalized to other example / rado losses, with necessary and sufficient conditions for the equivalence, exemplified on four losses that bear popular names in various fields: exponential (boosting), mean-variance (finance), Linear Hinge (on-line learning), ReLU (deep learning), and unhinged (statistics). Second, we show that the generalization unveils a surprising new connection to regularized learning, and in particular a sufficient condition under which regularizing the loss over examples is equivalent to regularizing the rados (with Minkowski sums) in the equivalent rado loss. This brings simple and powerful rado-based learning algorithms for sparsity-controlling regularization, that we exemplify on a boosting algorithm for the regularized exponential rado-loss, which formally boosts over four types of regularization, including the popular ridge and lasso, and the recently coined slope --- we obtain the first proven boosting algorithm for this last regularization. Through our first contribution on the equivalence of rado and example-based losses...

## Transfer Learning, Soft Distance-Based Bias, and the Hierarchical BOA

Pelikan, Martin; Hauschild, Mark W.; Lanzi, Pier Luca
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
35.71%
An automated technique has recently been proposed to transfer learning in the hierarchical Bayesian optimization algorithm (hBOA) based on distance-based statistics. The technique enables practitioners to improve hBOA efficiency by collecting statistics from probabilistic models obtained in previous hBOA runs and using the obtained statistics to bias future hBOA runs on similar problems. The purpose of this paper is threefold: (1) test the technique on several classes of NP-complete problems, including MAXSAT, spin glasses and minimum vertex cover; (2) demonstrate that the technique is effective even when previous runs were done on problems of different size; (3) provide empirical evidence that combining transfer learning with other efficiency enhancement techniques can often yield nearly multiplicative speedups.; Comment: Accepted at Parallel Problem Solving from Nature (PPSN XII), 10 pages. arXiv admin note: substantial text overlap with arXiv:1201.2241

## Using an Online Learning Environment to Teach an Undergraduate Statistics Course: the tutor-web

Jonsdottir, Anna Helga; Stefansson, Gunnar
Tipo: Artigo de Revista Científica
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## The two-dimensional Gabor function adapted to natural image statistics: An analytical model of simple-cell responses in the early visual system

Loxley, Peter
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
35.77%
The two-dimensional Gabor function is adapted to natural image statistics by learning the joint distribution of the Gabor function parameters. The joint distribution is then approximated to yield an analytical model of simple-cell receptive fields. Adapting a basis of Gabor functions is found to take an order of magnitude less computation than learning an equivalent non-parameterized basis. Derived learning rules are shown to be capable of adapting Gabor parameters to the statistics of images of man-made and natural environments. Learning is found to be most pronounced in three Gabor parameters that represent the size, aspect-ratio, and spatial frequency of the two-dimensional Gabor function. These three parameters are characterized by non-uniform marginal distributions with heavy tails -- most likely due to scale invariance in natural images -- and all three parameters are strongly correlated: resulting in a basis of multiscale Gabor functions with similar aspect-ratios, and size-dependent spatial frequencies. The Gabor orientation and phase parameters do not appear to gain anything from learning over natural images. Different tuning strategies are found by controlling learning through the Gabor parameter learning rates. Two opposing strategies include well-resolved orientation and well-resolved spatial frequency. On image reconstruction...

## On multi-view learning with additive models

Culp, Mark; Michailidis, George; Johnson, Kjell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
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In many scientific settings data can be naturally partitioned into variable groupings called views. Common examples include environmental (1st view) and genetic information (2nd view) in ecological applications, chemical (1st view) and biological (2nd view) data in drug discovery. Multi-view data also occur in text analysis and proteomics applications where one view consists of a graph with observations as the vertices and a weighted measure of pairwise similarity between observations as the edges. Further, in several of these applications the observations can be partitioned into two sets, one where the response is observed (labeled) and the other where the response is not (unlabeled). The problem for simultaneously addressing viewed data and incorporating unlabeled observations in training is referred to as multi-view transductive learning. In this work we introduce and study a comprehensive generalized fixed point additive modeling framework for multi-view transductive learning, where any view is represented by a linear smoother. The problem of view selection is discussed using a generalized Akaike Information Criterion, which provides an approach for testing the contribution of each view. An efficient implementation is provided for fitting these models with both backfitting and local-scoring type algorithms adjusted to semi-supervised graph-based learning. The proposed technique is assessed on both synthetic and real data sets and is shown to be competitive to state-of-the-art co-training and graph-based techniques.; Comment: Published in at http://dx.doi.org/10.1214/08-AOAS202 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

## Rates of convergence in active learning

Hanneke, Steve
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
35.71%
We study the rates of convergence in generalization error achievable by active learning under various types of label noise. Additionally, we study the general problem of model selection for active learning with a nested hierarchy of hypothesis classes and propose an algorithm whose error rate provably converges to the best achievable error among classifiers in the hierarchy at a rate adaptive to both the complexity of the optimal classifier and the noise conditions. In particular, we state sufficient conditions for these rates to be dramatically faster than those achievable by passive learning.; Comment: Published in at http://dx.doi.org/10.1214/10-AOS843 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

## Learning nonsingular phylogenies and hidden Markov models

Mossel, Elchanan; Roch, Sébastien
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
35.71%
In this paper we study the problem of learning phylogenies and hidden Markov models. We call a Markov model nonsingular if all transition matrices have determinants bounded away from 0 (and 1). We highlight the role of the nonsingularity condition for the learning problem. Learning hidden Markov models without the nonsingularity condition is at least as hard as learning parity with noise, a well-known learning problem conjectured to be computationally hard. On the other hand, we give a polynomial-time algorithm for learning nonsingular phylogenies and hidden Markov models.; Comment: Published at http://dx.doi.org/10.1214/105051606000000024 in the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org)

## Learning Active Basis Models by EM-Type Algorithms

Si, Zhangzhang; Gong, Haifeng; Zhu, Song-Chun; Wu, Ying Nian
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
35.75%
EM algorithm is a convenient tool for maximum likelihood model fitting when the data are incomplete or when there are latent variables or hidden states. In this review article we explain that EM algorithm is a natural computational scheme for learning image templates of object categories where the learning is not fully supervised. We represent an image template by an active basis model, which is a linear composition of a selected set of localized, elongated and oriented wavelet elements that are allowed to slightly perturb their locations and orientations to account for the deformations of object shapes. The model can be easily learned when the objects in the training images are of the same pose, and appear at the same location and scale. This is often called supervised learning. In the situation where the objects may appear at different unknown locations, orientations and scales in the training images, we have to incorporate the unknown locations, orientations and scales as latent variables into the image generation process, and learn the template by EM-type algorithms. The E-step imputes the unknown locations, orientations and scales based on the currently learned template. This step can be considered self-supervision, which involves using the current template to recognize the objects in the training images. The M-step then relearns the template based on the imputed locations...

## Approximation and learning by greedy algorithms

Barron, Andrew R.; Cohen, Albert; Dahmen, Wolfgang; DeVore, Ronald A.
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
35.73%
We consider the problem of approximating a given element $f$ from a Hilbert space $\mathcal{H}$ by means of greedy algorithms and the application of such procedures to the regression problem in statistical learning theory. We improve on the existing theory of convergence rates for both the orthogonal greedy algorithm and the relaxed greedy algorithm, as well as for the forward stepwise projection algorithm. For all these algorithms, we prove convergence results for a variety of function classes and not simply those that are related to the convex hull of the dictionary. We then show how these bounds for convergence rates lead to a new theory for the performance of greedy algorithms in learning. In particular, we build upon the results in [IEEE Trans. Inform. Theory 42 (1996) 2118--2132] to construct learning algorithms based on greedy approximations which are universally consistent and provide provable convergence rates for large classes of functions. The use of greedy algorithms in the context of learning is very appealing since it greatly reduces the computational burden when compared with standard model selection using general dictionaries.; Comment: Published in at http://dx.doi.org/10.1214/009053607000000631 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

## From Curriculum Guidelines to Learning Objectives: A Survey of Five Statistics Programs

Chance, Beth; Peck, Roxy
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
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The 2000 ASA Guidelines for Undergraduate Statistics majors aimed to provide guidance to programs with undergraduate degrees in statistics as to the content and skills that statistics majors should be learning. With new guidelines forthcoming, it is important to help programs develop an assessment cycle of evaluation. How do we know the students are learning what we want them to learn? How do we improve the program over time? The first step in this process is to translate the broader Guidelines into institution-specific measurable learning outcomes. This paper provides examples of how five programs did so for the 2000 Guidelines. We hope they serve as illustrative examples for programs moving forward with the new guidelines.

## Exploiting the Statistics of Learning and Inference

Welling, Max
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
45.58%
When dealing with datasets containing a billion instances or with simulations that require a supercomputer to execute, computational resources become part of the equation. We can improve the efficiency of learning and inference by exploiting their inherent statistical nature. We propose algorithms that exploit the redundancy of data relative to a model by subsampling data-cases for every update and reasoning about the uncertainty created in this process. In the context of learning we propose to test for the probability that a stochastically estimated gradient points more than 180 degrees in the wrong direction. In the context of MCMC sampling we use stochastic gradients to improve the efficiency of MCMC updates, and hypothesis tests based on adaptive mini-batches to decide whether to accept or reject a proposed parameter update. Finally, we argue that in the context of likelihood free MCMC one needs to store all the information revealed by all simulations, for instance in a Gaussian process. We conclude that Bayesian methods will remain to play a crucial role in the era of big data and big simulations, but only if we overcome a number of computational challenges.; Comment: Proceedings of the NIPS workshop on "Probabilistic Models for Big Data"

## Learning Class-Level Bayes Nets for Relational Data

Schulte, Oliver; Khosravi, Hassan; Moser, Flavia; Ester, Martin