The skilled team of specialists at AIDATO, use a comprehensive approach to care, which means we look at each aspect of your data to find out which variables are contributing to or exacerbating your data.
Transforming multidimensional data to a unit probabilistic hypercube
Operator norm second order moment minimization
Choosing the best kernel estimate
The function is given as z=F(x,y)
The given examples of approximation show convergence and precision process
The AIDATO method can beapplied to various classes of research tasks. Classification Task is one ofthem. Let's look at the accuracy and stability of themethod using the example of a classification problem and compare with otherknown methods.
Let’s define the following three geometrical zones
A set of random points are distributed as train points. This means that it is known exactly which zone each point belongs to. Note that the size and configuration of the zones are initially unknown to the methods
Now we randomly distribute new points and ask the mathematical method to "guess" which zone this point belongs to. Simulating this task several trails we can obtain the accuracy and stability of the methods
As you can see from the comparative analysis of the accuracy of different methods the AITATO method is more precise and stable methods
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The skilled team of specialists at AIDATO, use a comprehensive approach to care,
which means we look at each aspect of your data to find out which
variables are contributing to or exacerbating your data.
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