860 resultados para feature advertising
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In this paper, we propose a novel filter for feature selection. Such filter relies on the estimation of the mutual information between features and classes. We bypass the estimation of the probability density function with the aid of the entropic-graphs approximation of Rényi entropy, and the subsequent approximation of the Shannon one. The complexity of such bypassing process does not depend on the number of dimensions but on the number of patterns/samples, and thus the curse of dimensionality is circumvented. We show that it is then possible to outperform a greedy algorithm based on the maximal relevance and minimal redundancy criterion. We successfully test our method both in the contexts of image classification and microarray data classification.
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Nowadays, consumers are faced with a variety of media that convey myriad advertising messages, which struggle amidst a highly competitive environment, with a view to drawing the viewers’ attention, raising awareness, creating interest and inspiring desire and, ultimately, leading to the purchase of the product/service at stake. For this, advertising professionals deliberately intertwine their selling arguments with emotionally-charged creative concepts. It is the aim of this study to analyse the impact of the main creative appeals and to identify groups of consumers based on their attitudes towards them. We have undertaken a quantitative study, by means of a survey administered to a convenience sample with a list of creative appeals, which had to be classified by the respondents according to their attitudes. Globally speaking, the preferred appeals were humour, music and animation. Nonetheless, it was possible to divide the respondents into three groups. ‘Advertising fans’, the ‘rationally-minded’ and the ‘emotionally-minded’. This study presents some limitations, especially as to the sample used. Apart from the reduced number of respondents and lack of more widespread geographic reach, some academic qualifications were underrepresented. The results of this study offer some avenues to be explored by marketing and advertising professionals when it comes to deciding on the best creative approach to select for their advertising campaigns. Besides, this study paves the way to the development of future research on the issue of advertising appeals and its relationship with the psychographic characteristics of consumers.
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Objective: To review the scientific literature on pharmaceutical advertising aimed at health professionals in order to determine whether gender bias has decreased and the quality of information in pharmaceutical advertising has improved over time. Methods: We performed a content analysis of original articles dealing with medical drug promotion (1998-2008), according to quality criteria such as (a) the number, validity and accessibility of bibliographic references provided in pharmaceutical advertising and (b) the extent to which gender representations were consistent with the prevalence of the diseases. Databases: PUBMED, Medline, Scopus, Sociological Abstract, Eric and LILACS. Results: We reviewed 31 articles that analyzed advertising in medical journals from 1975-2005 and were published between 1998 and 2008. We found that the number of references used to support pharmaceutical advertising claims increased from 1975 but that 50% of these references were not valid. There was a tendency to depict men in paid productive roles, while women appeared inside the home or in non-occupational social contexts. Advertisements for psychotropic and cardiovascular drugs overrepresented women and men respectively. Conclusions: The use of bibliographic references increased between 1998 and 2008. However, representation of traditional male-female roles was similar in 1975 and 2005. Pharmaceutical advertisements may contribute to reinforcing the perception that certain diseases are associated with the most frequently portrayed sex.
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En este artículo se investigan técnicas automáticas para encontrar un modelo óptimo de características en el caso de un analizador de dependencias basado en transiciones. Mostramos un estudio comparativo entre algoritmos de búsqueda, sistemas de validación y reglas de decisión demostrando al mismo tiempo que usando nuestros métodos es posible conseguir modelos complejos que proporcionan mejores resultados que los modelos que siguen configuraciones por defecto.
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Many destination marketing organizations in the United States and elsewhere are facing budget retrenchment for tourism marketing, especially for advertising. This study evaluates a three-stage model using Random Coefficient Logit (RCL) approach which controls for correlations between different non-independent alternatives and considers heterogeneity within individual’s responses to advertising. The results of this study indicate that the proposed RCL model results in a significantly better fit as compared to traditional logit models, and indicates that tourism advertising significantly influences tourist decisions with several variables (age, income, distance and Internet access) moderating these decisions differently depending on decision stage and product type. These findings suggest that this approach provides a better foundation for assessing, and in turn, designing more effective advertising campaigns.
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Several recent works deal with 3D data in mobile robotic problems, e.g. mapping or egomotion. Data comes from any kind of sensor such as stereo vision systems, time of flight cameras or 3D lasers, providing a huge amount of unorganized 3D data. In this paper, we describe an efficient method to build complete 3D models from a Growing Neural Gas (GNG). The GNG is applied to the 3D raw data and it reduces both the subjacent error and the number of points, keeping the topology of the 3D data. The GNG output is then used in a 3D feature extraction method. We have performed a deep study in which we quantitatively show that the use of GNG improves the 3D feature extraction method. We also show that our method can be applied to any kind of 3D data. The 3D features obtained are used as input in an Iterative Closest Point (ICP)-like method to compute the 6DoF movement performed by a mobile robot. A comparison with standard ICP is performed, showing that the use of GNG improves the results. Final results of 3D mapping from the egomotion calculated are also shown.
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Paper submitted to the 39th International Symposium on Robotics ISR 2008, Seoul, South Korea, October 15-17, 2008.
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High resolution X-ray spectroscopy is a powerful tool for studying the nature of the matter surrounding the neutron star in X-ray binaries and its interaction between the stellar wind and the compact object. In particular, absorption features in their spectra could reveal the presence of atmospheres of the neutron star or their magnetic field strength. Here we present an investigation of the absorption feature at 2.1 keV in the X-ray spectrum of the high mass X-ray binary 4U 1538–52 based on our previous analysis of the XMM-Newton data. We study various possible origins and discuss the different physical scenarios in order to explain this feature. A likely interpretation is that the feature is associated with atomic transitions in an O/Ne neutron star atmosphere or of hydrogen and helium like Fe or Si ions formed in the stellar wind of the donor.
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Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multi-objective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.
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Nowadays, online media represent a great choice for advertising. From de advertising media planning, new media give new ways to reach the consumers, but they also add more complexity. The communication capacity of online media and the greater use of that media by part of the users open up the debate about the necessity of rethinking the approach of the ‘traditional’ advertising media planning, which structure and work processes were developed when media were offline. So, this article gives a panoramic view about the influence of new media in advertising media planning. To do this, in first place, describes the current scenario, analyzing the penetration and advertising expenditure in Internet. Also, it shows the main online media according to their proximity to the offline advertising media planning conception. In second place, this article addresses the current challenges at measuring new media as a symptom of the impulse at the change of model. Finally, the article ends up showing some trends that are presented as drivers of change. However, after this analysis, comes up the point that those aspects would not change the essence of advertising media planning, so it is questionable if we can speak of a crisis or, instead, if new media are showing the necessity that media planning have to be involved with this new scenario.
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Background To analyse the scientific evidence that exists for the advertising claims made for two products containing Lactobacillus casei and Bifidobacterium lactis and to conduct a comparison between the published literature and what is presented in the corporate website. Methods Systematic review, using Medline through Pubmed and Embase. We included human clinical trials that exclusively measured the effect of Lactobacillus casei or Bifidobacterium lactis on a healthy population, and where the objective was related to the health claims made for certain products in advertising. We assessed the levels of evidence and the strength of the recommendation according to the classification criteria established by the Oxford Centre for Evidence Based Medicine (CEBM). We also assessed the outcomes of the studies published on the website that did not appear in the search. Results Of the 440 articles identified, 16 met the inclusion criteria. Only four (25%) of these presented a level of evidence of 1b and a recommendation grade of A, all corresponding to studies on product containing Bifidobacterium lactis, and only 12 of the 16 studies were published on the corporate website (47). Conclusions There is insufficient scientific evidence to support the health claims made for these products, especially in the case of product containing Lactobacillus casei.
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In a context of intense competition, cooperative advertising between firms is critical. Accordingly, the objective of this article is to analyze the potential differentiated effect of advertising on two basic consumption patterns: individual products (i.e. hotel, restaurant) vs. bundle (i.e. hotel + restaurant). This research adds to the extant literature in that, for the first time, this potential differentiated effect is examined through a hierarchical modelling framework that reflects the way people make their decisions: first, they decide whether to visit or not a region; second, whether to purchase an advertised product in that region; and third, whether to buy products together or separately at the region. The empirical analysis, applied to a sample of 11,288 individuals, shows that the influence of advertising is positive for the decisions to visit and to purchase; however, when it comes to the joint or separate consumption, advertising has a differentiated effect: its impact is much greater on the joint alternative (“hotel + restaurant”) than the separate options (“hotel” and “restaurant”). Also, the variable distance moderates the advertising effect.
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From the perspective of the sociology of professions, every professional activity should have its own clearly circumscribed and regulated sphere of action. Such an articulation facilitates the regulation of the production of a given profession as well as the way in which it is practiced. The purpose of the research reported here was to provide a comprehensive review and evaluation of the regulatory framework governing the advertising sector in Spain. To this end, the authors analysed external regulatory legislation and self-regulatory codes extracted from the data base of the Asociación para la Autoregulación de la Comunicación Comercial (Autocontrol) that had been enacted or adopted between 1988, the year that Law 11/1998 on General Telecommunications entered into force, and 2003 as well as other relevant documents retrieved from the Boletin Oficial del Estado (BOE) pertaining to the same period. Findings indicate that although there has been a groundswell of legislation governing advertising practices in Spain since 1988, especially at the regional level, lawmakers have focused on the content of advertising messages and shown very little interest in regulating the professions of advertising and public relations. Furthermore, Spanish legislation enacted in 2003 and EU policies appear to have encouraged the adoption of voluntary codes of ethics. Sectors traditionally subject to mandatory advertising regulation, either due to the vulnerability of their target audiences or the potential impact of their commercial messages on public health or the environment, are more likely to develop self-regulatory codes of conduct than others
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We study the timing and spectral properties of the low-magnetic field, transient magnetar SWIFT J1822.3−1606 as it approached quiescence. We coherently phase-connect the observations over a time-span of ∼500 d since the discovery of SWIFT J1822.3−1606 following the Swift-Burst Alert Telescope (BAT) trigger on 2011 July 14, and carried out a detailed pulse phase spectroscopy along the outburst decay. We follow the spectral evolution of different pulse phase intervals and find a phase and energy-variable spectral feature, which we interpret as proton cyclotron resonant scattering of soft photon from currents circulating in a strong (≳1014 G) small-scale component of the magnetic field near the neutron star surface, superimposed to the much weaker (∼3 × 1013 G) magnetic field. We discuss also the implications of the pulse-resolved spectral analysis for the emission regions on the surface of the cooling magnetar.
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In many classification problems, it is necessary to consider the specific location of an n-dimensional space from which features have been calculated. For example, considering the location of features extracted from specific areas of a two-dimensional space, as an image, could improve the understanding of a scene for a video surveillance system. In the same way, the same features extracted from different locations could mean different actions for a 3D HCI system. In this paper, we present a self-organizing feature map able to preserve the topology of locations of an n-dimensional space in which the vector of features have been extracted. The main contribution is to implicitly preserving the topology of the original space because considering the locations of the extracted features and their topology could ease the solution to certain problems. Specifically, the paper proposes the n-dimensional constrained self-organizing map preserving the input topology (nD-SOM-PINT). Features in adjacent areas of the n-dimensional space, used to extract the feature vectors, are explicitly in adjacent areas of the nD-SOM-PINT constraining the neural network structure and learning. As a study case, the neural network has been instantiate to represent and classify features as trajectories extracted from a sequence of images into a high level of semantic understanding. Experiments have been thoroughly carried out using the CAVIAR datasets (Corridor, Frontal and Inria) taken into account the global behaviour of an individual in order to validate the ability to preserve the topology of the two-dimensional space to obtain high-performance classification for trajectory classification in contrast of non-considering the location of features. Moreover, a brief example has been included to focus on validate the nD-SOM-PINT proposal in other domain than the individual trajectory. Results confirm the high accuracy of the nD-SOM-PINT outperforming previous methods aimed to classify the same datasets.