Consistency of plug-in confidence sets for classification in semi-supervised learning

Confident prediction is highly relevant in machine learning; for example, in applications such as medical diagnoses, wrong prediction can be fatal. For classification, there already exist procedures that allow to not classify data when the confidence in their prediction is weak. This approach is known as classification with reject option. In the present paper, we provide new methodology for this approach. Predicting a new instance via a confidence set, we ensure an exact control of the probability of classification. Moreover, we show that this methodology is easily implementable and entails attractive theoretical and numerical properties

Classification in postural style

This article contributes to the search for a notion of postural style, focusing on the issue of classifying subjects in terms of how they maintain posture. Longer term, the hope is to make it possible to determine on a case by case basis which sensorial information is prevalent in postural control, and to improve/adapt protocols for functional rehabilitation among those who show deficits in maintaining posture, typically seniors. Here, we specifically tackle the statistical problem of classifying subjects sampled from a two-class population. Each subject (enrolled in a cohort of 54 participants) undergoes four experimental protocols which are designed to evaluate potential deficits in maintaining posture. These protocols result in four complex trajectories, from which we can extract four small-dimensional summary measures. Because undergoing several protocols can be unpleasant, and sometimes painful, we try to limit the number of protocols needed for the classification. Therefore, we first rank the protocols by decreasing order of relevance, then we derive four plug-in classifiers which involve the best (i.e., more informative), the two best, the three best and all four protocols. This two-step procedure relies on the cutting-edge methodologies of targeted maximum likelihood learning (a methodology for robust and efficient inference) and super-learning (a machine learning procedure for aggregating various estimation procedures into a single better estimation procedure). A simulation study is carried out. The performances of the procedure applied to the real data set (and evaluated by the leave-one-out rule) go as high as an 87% rate of correct classification (47 out of 54 subjects correctly classified), using only the best protocol.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS542 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

ACCESSING REFERENTIAL INFORMATION DURING TEXT COMPOSITION : WHEN AND WHY ?

When composing a text, writers have to continually shift between content planning and content translating. This continuous shifting gives the writing activity its cyclic nature. The first section of this paper will analyse the writing process as a hierarchical cyclic activity. A methodological paradigm will be proposed for the investigation of the writing process. In the second section, we will partially present two experiments that were conducted independently, with this paradigm. Both give a coherent and interesting picture of what happens with content while the writer is planning. The characteristics of cycles depend both on the nature of the content information being recovered and on the complexity of the processes applied to this content

Multiple radial positive solutions of semilinear elliptic problems with Neumann boundary conditions

Assuming $B_{R}$ is a ball in $\mathbb R^{N}$, we analyze the positive solutions of the problem $$\begin{cases} -\Delta u+u= |u|^{p-2}u, &\text{ in } B_{R},\newline \partial_{\nu}u=0,&\text{ on } \partial B_{R}, \end{cases}$$ that branch out from the constant solution $u=1$ as $p$ grows from $2$ to $+\infty$. The non-zero constant positive solution is the unique positive solution for $p$ close to $2$. We show that there exist arbitrarily many positive solutions as $p\to\infty$ (in particular, for supercritical exponents) or as $R \to \infty$ for any fixed value of $p>2$, answering partially a conjecture in [Bonheure-Noris-Weth]. We give the explicit lower bounds for $p$ and $R$ so that a given number of solutions exist. The geometrical properties of those solutions are studied and illustrated numerically. Our simulations motivate additional conjectures. The structure of the least energy solutions (among all or only among radial solutions) and other related problems are also discussed.Comment: 37 pages, 24 figure

Do Convolutional Networks need to be Deep for Text Classification ?

We study in this work the importance of depth in convolutional models for text classification, either when character or word inputs are considered. We show on 5 standard text classification and sentiment analysis tasks that deep models indeed give better performances than shallow networks when the text input is represented as a sequence of characters. However, a simple shallow-and-wide network outperforms deep models such as DenseNet with word inputs. Our shallow word model further establishes new state-of-the-art performances on two datasets: Yelp Binary (95.9\%) and Yelp Full (64.9\%)

Management Tools for RetD Project Portfolios in Complex Organizations – the case of an international pharmaceutical firm

Project Portfolio Management (PPM) is a growing issue in both professional and academic circles. The typology of Cooper et al. (1998) has pictured the variety of PPM formalized approaches into four types (financial, strategic, scoring and “bubble diagram”). While the use of formalized methods by top performers is clearly attested, the choice of a specific approach and the precise benefits and limits of different instruments are still in debate. The present paper formalizes more precise contingency hypotheses between PPM practices and organizational variables such as R&D strategy, the structure and history of a firm's development, partnership policy and learning track in the project domain. Where managerial implications are concerned, the paper puts forward an analytical framework for the adjustment of portfolio instruments to fit specific situations and develops the conclusions of that framework for an international pharmaceutical group, Merck Lipha. The research underlying this paper adopts an interactive and experimental case-based methodology which has been on-going since 1997.Project; portfolios; pharmaceuticals; decision; processes; interactive research

Robust control of a bimorph mirror for adaptive optics system

We apply robust control technics to an adaptive optics system including a dynamic model of the deformable mirror. The dynamic model of the mirror is a modification of the usual plate equation. We propose also a state-space approach to model the turbulent phase. A continuous time control of our model is suggested taking into account the frequential behavior of the turbulent phase. An H_\infty controller is designed in an infinite dimensional setting. Due to the multivariable nature of the control problem involved in adaptive optics systems, a significant improvement is obtained with respect to traditional single input single output methods