منابع مشابه
The generation of fuzzy sets and the~construction of~characterizing functions of~fuzzy data
Measurement results contain different kinds of uncertainty. Besides systematic errors andrandom errors individual measurement results are also subject to another type of uncertainty,so-called emph{fuzziness}. It turns out that special fuzzy subsets of the set of real numbers $RR$are useful to model fuzziness of measurement results. These fuzzy subsets $x^*$ are called emph{fuzzy numbers}. The m...
متن کاملSelection of Variables that Influence Drug Injection in Prison: Comparison of Methods with Multiple Imputed Data Sets
Background: Prisoners, compared to the general population, are at greater risk of infection. Drug injection is the main route of HIV transmission, in particular in Iran. What would be of interest is to determine variables that govern drug injection among prisoners. However, one of the issues that challenge model building is incomplete national data sets. In this paper, we addressed the process ...
متن کاملMultiple target tracking based on sets of trajectories
This paper proposes the set of target trajectories as the state variable for multiple target tracking. The main objective of multiple target tracking is to estimate an unknown number of target trajectories given a sequence of measurements. This quantity of interest is perfectly represented as a set of trajectories without the need of arbitrary parameters such as labels or ordering. We use finit...
متن کاملIntegrative data analysis: the simultaneous analysis of multiple data sets.
There are both quantitative and methodological techniques that foster the development and maintenance of a cumulative knowledge base within the psychological sciences. Most noteworthy of these techniques is meta-analysis, which allows for the synthesis of summary statistics drawn from multiple studies when the original data are not available. However, when the original data can be obtained from...
متن کاملExtracting Coactivated Features from Multiple Data Sets
We present a nonlinear generalization of Canonical Correlation Analysis (CCA) to find related structure in multiple data sets. The new method allows to analyze an arbitrary number of data sets, and the extracted features capture higher-order statistical dependencies. The features are independent components that are coupled across the data sets. The coupling takes the form of coactivation (depen...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Journal of the Acoustical Society of America
سال: 1982
ISSN: 0001-4966
DOI: 10.1121/1.2019853