We study the problem of cross-domain ranking, which addresses learning to rank objects from multiple interrelated domains. In many applications, we may have multiple interrelated domains, some of them with a large amount of training data and others with very little. We often wish to utilize the training data from all these related domains to help improve ranking performance. In this paper, we p...

Human gait has been shown to be an efficient biometric measure for person identification at a distance. However, it often needs different gait features to handle various covariate conditions including viewing angles, walking speed, carrying an object and wearing different types of shoes. In order to improve the robustness of gait-based person re-identification on such multi-covariate conditions...

We study the problem of context-sensitive ranking for document retrieval, where a context is defined as a sub-collection of documents, and is specified by queries provided by domain-interested users. The motivation of context-sensitive search is that the ranking of the same keyword query generally depends significantly on the context. The underlying reason is that the underlying keyword statist...

The main purpose of this paper is to propose an approach for performance measurement, classification and ranking the investment companies (ICs) by considering internal structure and uncertainty. In order to reach this goal, the interval network data envelopment analysis (INDEA) models are extended. This model is capable to model two-stage efficiency with intermediate measures i...

the present study is an attempt toward evaluating the performance of portfolios and asset selectionusing cross-efficiency evaluation. cross-efficiency evaluation is an effective way of ranking decisionmaking units (dmus) in data envelopment analysis (dea). conventional dea models assume nonnegativevalues for inputs and outputs. however, we know that unlike return and skewness, varianceis the on...

Microaggregation by individual ranking is one of the most commonly applied disclosure control techniques for continuous microdata. The paper studies the effect of microaggregation by individual ranking on the least squares estimation of a multiple linear regression model in continuous variables. It is shown that the naive parameter estimates are asymptotically unbiased. Moreover, the naive leas...

Keywords: Data envelopment analysis (DEA) Common weights analysis (CWA) Ranking The ideal line The special line a b s t r a c t Conventional data envelopment analysis (DEA) assists decision makers in distinguishing between efficient and inefficient decision making units (DMUs) in a homogeneous group. However, DEA does not provide more information about the efficient DMUs. In this research, the ...

The paper deals with the introduction of Bertin’s visual variables in an ATC context. The ranking of the efficiency of these variables has been experimentally verified by Cleveland, however, no studies highlight the physiological correlates of this ranking. We analyzed behavioral, physiological and subjective data recorded on 7 healthy subjects facing a visual comparison task witch involve 5 se...