data coupling,multi-modeling methods integration,multi-resolution visualization and detection,and multidimensional data analysis and application.By analyzing problems such as the uncertainty in each step of the modeling process,we designed a novel modeling method that can be applied to the complex ...
posit in a database Three-dimensional modeling of a deposit in a databaseThree-dimensional modeling of a deposit in a databaseAbstract Without Abstractdoi:10.1007/BF02500786I. D. ChashchinKluwer Academic Publishers-Plenum PublishersJournal of Mining Science...
Data mining and machine learning techniques for processing raster data consider a single spectral band of data at a time. The individual results are combined to obtain the final output. The essence of related multi-spectral information is lost when the bands are considered independently. The propose...
A Python toolbox for gaining geometric insights into high-dimensional data visualizationpythontime-seriesdata-visualizationhigh-dimensional-datatopic-modelingdata-wranglingtext-vectorization UpdatedMar 19, 2024 Python vdaas/vald Star1.6k Code Issues
Finally, a simple visual economic model of a real country is constructed and analyzed in order to illustrate the special strength of ∥-coords in modeling multivariate relations by means of hypersurfaces.doi:10.1016/S0169-7439(01)00192-7Alfred Inselberg...
To the best of our knowledge, 3D full-loop modeling of an industrial scale CFB boiler involving erosion behavior has been seldom reported in the previous literature. In addition, the research and operating data of the USC CFB boiler have been insufficient up to now; studies on this boiler hav...
This paper introduces the architecture and algorithms of TCMiner: a high performance data mining system for multi-dimensional data analysis of Traditional Chinese Medicine prescriptions. The system ha C Li,C Tang,J Peng,... - International Conference on Conceptual Modeling 被引量: 37发表: 2004年...
which a user is commonly interested in aggregating (such as 'Sales Amount' or 'Quota), are then presented as 'measures.' This method of presenting the data, as 'measures' and 'dimensions,' is what is known as Dimensional Modeling, and has been shown in the industry to be a successful ...
For example, longitudinal data are often modeled with mixed models, which allow to properly account for within-subject correlations. Feature selection (a.k.a. variable selection) in predictive modeling can be defined as the task of selecting one or more minimal-size and (collectively) optimally ...
several image classification tasks. Grover et al. [21] use a hybrid approach to improve performance on modeling a set of weather variables. Their approach uses the outputs of traditional predictors of the variables as input to a neural network to refine those predictions. Wang et al. [22] ...