1,721,206 research outputs found
Un ventre, deux estomacs et une partie de tête : Les parties du corps entre massif et comptable : des relations partie/tout ?
No full textGreco Luca. Un ventre, deux estomacs et une partie de tête : Les parties du corps entre massif et comptable : des relations partie/tout ?. In: L'Information Grammaticale, N. 83, 1999. pp. 29-31
Robust inference in composite transformation models
Full text linkThe aim of this paper is to base robust inference about a shape parameter indexing a composite transformation model on a quasi- prole likelihood ratio test statistic. First, a general procedure is presented in order to construct a bounded prole estimating function for shape parameters. This method is based on a standard truncation argument from the theory of robustness. Hence, a quasi-likelihood test is derived. Numerical studies and applications to real data show that its use reveals extremely powerful, leading to improved inferences with respect to classical robust Wald and score-type test statistics
A plug-in approach to sparse and robust principal component analysis
Full text linkWe propose a method for sparse and robust principal component analysis.
The methodology is structured in two steps: first, a robust estimate of the covariance
matrix is obtained, then this estimate is plugged-in into an elastic-net regression which
enforces sparseness. Our approach provides an intuitive, general and flexible extension
of sparse principal component analysis to the robust setting. We also show how to
implement the algorithm when the dimensionality exceeds the number of observations
by adapting the approach to the use of robust loadings from ROBPCA. The proposed
technique is seen to compare well for simulated and real datasets
A “Mobile Virtual Lab” for Supporting Engineering Curricula
No full textE-Learning is offering new approaches and opportunities in the field of education
above all in the sector of the virtual laboratories. The opportunity to interact with the real
instruments that are in a lab represents an effective way for the implementation of a teaching
approach oriented to a problem solving strategy. The wide diffusion of mobile devices
(smartphones and tablets) makes this approach more effective and appealing for the students. In
this paper we introduce Jini Technologies to design and implement a distributed architecture for
a mobile virtual lab for supporting the activities of an Electronic Measurement Course. The aim
of this environment is offering to the students the opportunity to experiment a real interaction
with the laboratory’s instruments everywhere and every time by the use of mobile devices. A
prototype of the architecture and its services will be discussed and an evaluation campaign will
be showe
An adaptive product configurator based on slow intelligence approach
No full textIn today’s competitive global market, understanding quickly customers’ needs is a primary task for a successful design and implementation of customised products. For this aim an
effective approach is the adoption of a product configurator. The product configuration is the activity of customising a product to meet the needs of a particular customer that, usually, expresses his/her preferences by the use of the natural language. In this scenario a challenging problem is the definition of a common vocabulary that opens possibilities such as the ability to express the customer requirements and to exchange knowledge. For this aim the adoption of the
ontological formalism is an effective approach. Ontology, in fact, is a key factor for enabling interoperability across heterogeneous systems, services and users. This paper presents an ontology-based configurator system that maximally satisfies users’ needs starting from the
desired requirements, the available components, the context information and previous similar configurations
SAFE: A Sentiment Analysis Framework for E-Learning
Full text linkThe spread of social networks allows sharing opinions on different aspects of life and daily millions of messages appear on the web. This textual information can be a rich source of data for opinion mining and sentiment analysis: the computational study of opinions, sentiments and emotions expressed in a text. Its main aim is the identification of the agreement or disagreement statements that deal with positive or negative feelings in comments or reviews. In this paper, we investigate the adoption, in the field of the e-learning, of a probabilistic approach based on the Latent Dirichlet Allocation (LDA) as Sentiment grabber. By this approach, for a set of documents belonging to a same knowledge domain, a graph, the Mixed Graph of Terms, can be automatically extracted. The paper shows how this graph contains a set of weighted word pairs, which are discriminative for sentiment classification. In this way, the system can detect the feeling of students on some topics and teacher can better tune his/her teaching approach. In fact, the proposed method has been tested on datasets coming from e-learning platforms. A preliminary experimental campaign shows how the proposed approach is effective and satisfactory
Learning Bayesian Network Structure Using a MultiExpert Approach
No full textThe learning of a Bayesian network structure, especially in the case of wide domains, can be a complex, time-consuming and imprecise process. Therefore, the interest of the scientific community in learning Bayesian network structure from data is increasing: many techniques or disciplines such as data mining, text categorization, and ontology building, can take advantage from this process. In the literature, there are many structural learning algorithms but none of them provides good results for each dataset. This paper introduces a method for structural learning of Bayesian networks based on a MultiExpert approach. The proposed method combines five structural learning algorithms according to a majority vote combining rule for maximizing their effectiveness and, more generally, the results obtained by using of a single algorithm. This paper shows an experimental validation of the proposed algorithm on standard datasets
Adjusted quasi-profile likelihoods from estimating functions
Full text linkHigher-order adjustments for a quasi-profile likelihood for a scalar parameter of interest in the presence of nuisance parameters are discussed.
Paralleling likelihood asymptotics, these adjustments aim to alleviate some of the problems inherent to the presence of nuisance parameters. Indeed, the estimating equation for the parameter of interest, when the nuisance parameter is substituted with an appropriate estimate, is not unbiased and such a bias can lead to poor inference on the parameter of interest. Following the approach of McCullagh and Tibshirani (1990), here we propose adjustments for the estimating equation for the parameter of interest. Moreover, we discuss two methods for their computation: a bootstrap simulation method, and a first-order asymptotic expression, which can be simplified under an orthogonality assumption. Some examples, in the context of generalized linear models and of robust inference, are provided
- …
