Hence, it is necessary to distill the data, in order to get the advantages of the data analysis techniques and extract the essential knowledge from the data. Due to the massive data storage and the diversity of the data analysis methods, various challenges are arisen during the process of ...
The observed patterns in the hydrogen ion data can be completely explained in terms of a stationary ARIMA model.” It should be pointed out, however, that they never formally tested for a long-term trend. For example, in Chapter 1 we studied methods for testing a linear trend in series of...
The application of multivariate statistical methods to high mountain lake monitoring data has offered some important conclusions about the importance of en... P Simeonova,V Lovchinov,D Dimitrov,... - 《Environmental Monitoring & Assessment》 被引量: 17发表: 2010年 Relationships among nekton assembla...
XmdvTool: Integrating multiple methods for visualizing multivariate data. In Proc. IEEE visualization (pp. 326–336). Wohlfart, M., & Hauser, H. (2007). Story telling for presentation in volume visualization. In EuroVis2007. About this Chapter Title Visual Storytelling in Education Applied...
Recurrent neural networks (RNNs) are nonlinear dynamical models commonly used in the machine learning and dynamical systems literature to represent complex dynamical or sequential relationships between variables. Recently, as deep learning models have be
Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 2017, 202, 18–27. [Google Scholar] [CrossRef] Rodriguez-Galiano, V.F.; Chica-Rivas, M. Evaluation of different machine learning methods for land cover mapping of a Mediterranean area using multi...
Various embodiments of the present technology generally relate to data delivery. More specifically, some embodiments of the present technology relate to systems and methods for usin
Tensor decomposition can be originated with the contribution of Hitchcock, 1927a, Hichcock, 1927b, and it was not until the work of Tucker (1966) that the tensor methods became more practical for data analysis. Nowadays, there are mainly two classical formulations of tensor decomposition, the ...
Then, we present the introduce of datasets, baseline methods and experimental settings in Sections 5. Next, we evaluate the effectiveness comparison and efficiency analysis of the proposed model in Section 6. Last, we give the conclusion of this work in Section 7. All source codes of this ...
University, 2000–2002: Statistical Inference, Applied Statistical Methods, Resampling and Bootstrap, Stochastic Process, Time Series Analysis, Multivariate Analysis, Analysis of Discrete Data, Regression Models and Analysis of Variance, Nonparametric Statistics, and Geostatistics. Interned in the Statistical...