Adaptive Gaussian Filtering is a simple and powerful implementation of variable bandwidth kernel estimators for classification, PDF estimation and interpolation. The library incorporates several innovations to produce one of the fastest and most accurate supervised statistical classification algorithms in the world. These include: matching kernel width to sample density quickly and accurately restricting calculations to a set of k-nearest-neighbours found in O(n) time generating a pre-trained model by searching for the class-borders with guaranteed, superlinear convergence extrapolating the conditional probabilities to provide solid knowledge of estimate accuracy (Source: http://freecode.com/)
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Peherstorfer, Benjamin; Willcox, Karen; Gunzburger, Max: Survey of multifidelity methods in uncertainty propagation, inference, and optimization (2018)
- Peherstorfer, Benjamin; Kramer, Boris; Willcox, Karen: Combining multiple surrogate models to accelerate failure probability estimation with expensive high-fidelity models (2017)
- Franzelin, Fabian; Pflüger, Dirk: From data to uncertainty: an efficient integrated data-driven sparse grid approach to propagate uncertainty (2016)
Further publications can be found at: http://peteysoft.users.sourceforge.net/publications.html