SearchSkip to Search Results
- 3Kernel density estimation
- 1Condition monitoring
- 1Data generator with hierarchical ground truth
- 1Data integration
- 1Density-based clustering
Integration and Evaluation of Different Kernel Density Estimates in Hierarchical Density-Based ClusteringDownload
Most machine learning methods make assumptions about data. Parametric statistics assume that the data is sampled from a distribution with fixed properties set by the algorithm or user. In contrast, non-parametric statistics do not assume the properties of a distribution. Instead, they assume that...
Improved numerical reservoir models are constructed when all available diverse data sources are accounted for to the maximum extent possible. Integrating various diverse data is not a simple problem because data show different precision and relevance to the primary variables being modeled,...
Reliability estimation based on condition monitoring data contains two important parts: thresholding and probability density estimation. Thresholding is to determine a critical level of an indicator corresponding to the transition of system states. Probability density estimation is to estimate...