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In this work, we show that by using a recursive random forest together with an alpha beta filter classifier, it is possible to classify radar tracks from the tracks' kinematic data.
It has been shown in [1] that it is possible to classify tracks by using a recursive classifier where a Gaussian mixture model (GMM) is used to model the probability distribution function (PDF) of the target's kinematic behavior.
Despite the extreme diversity of international agreements, it is possible to classify them according to the functions that they serve in international society.
From these samples, it is possible to classify the refractory failure into six zones by utilizing the penetration depth (calculated by area) coupled with simulations of particle trajectories.
With the proposed procedure, it is possible to classify soils as either 'clearly safe' or 'likely to result in significant damage if liquefied' by testing a small number of specimens.
In fact, it is possible to classify the deposited films as large grained (~ 15 nm) with a reduced lattice constant (~ 4.15 Å) and low compressive residual stresses for lower applied substrate biases, and as small grained (~ 4 nm) with an increased lattice constant (~ 4.25 Å) and high compressive residual stresses for applied biases of − 100 V or more.
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In this way, it was possible to classify these processes as regular deposition.
Based on these slope values it might be possible to classify several corroding systems according to the pit density.
By monitoring a wide variety of products as they are cooked in the oven it has been possible to classify their cooking stage.
Using discriminant statistical techniques it was possible to classify, in PM muscle and liver, 100% of the pigs fed with chestnut.
By applying selected molecular descriptors it was possible to classify active and inactive compounds with accuracy up to 92% and also to suggest the activity of new untested molecules.
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