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The conceptual framework proposes that FinTech has three dimensions – input, mechanisms, and output and acts as a transformation machine.
In the scholarly debate democratic legitimacy includes two dimensions: input and output legitimacy (Scharpf, 1999).
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The polynomial kernel can map low-dimension input space to high-dimension feature space.
The Fig. 4c shows how the SPLS, in dimension input, were updated along the learning process.
So, there are six possible data organizations of the input data depending on which dimension input data will be aligned.
The Kohonen neural network architecture used was 5×5 neurons, thus reducing 14-dimension input data to two-dimensional space.
Since other classifiers need to have the same dimension input data, we use the LBG algorithm to quantize features to the same dimension.
The basic structure of an RBF neural network includes an n dimension input layer, a fairly larger dimension m hidden layer (m > n) and the output layer.
Training of the algorithm will be carried out using the newly generated higher dimension input data matrix and an optimized relation between the output and input variables will be generated using RLS.
So that we can create a map from all these n-dimension inputs and keep the topology for further usage.
The dimension (inputs and outputs) ranged from four to ten, with a median of six variables per model.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com