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Classication in high-dimensional feature spaces where interpretation and dimension reduction are of great importance is common in biological and medical applications. For these applications standard methods as microarrays, 1D NMR, and spectroscopy have become everyday tools for measuring thousands of features in samples of interest. Furthermore, the samples(More)
Statistical solutions find wide spread use in food and medicine quality control. We investigate the effect of different regression and sparse regression methods for a viscosity estimation problem using the spectro-temporal features from new Sub-Surface Laser Scattering (SLS) vision system. From this investigation, we propose the optimal solution for(More)
Texture synthesis algorithms have been researched extensively in the past decade. However, most synthesis algorithms are governed by a set of parameters and produce different results depending on which parameter settings are chosen in conjunction with an exem-plar used as a basis for synthesis. So far, automatically selecting parameters suitable for(More)
We propose a novel methodology, forest floor, to visualize and interpret random forest (RF) models. RF is a popular and useful tool for non-linear multi-variate classification and regression, which yields a good trade-off between robustness (low variance) and adaptiveness (low bias). Direct interpretation of a RF model is difficult, as the explicit ensemble(More)
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