Difference between revisions of "General Regression Neural Network (GRNN)"

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[http://www.youtube.com/results?search_query=Neural+Network+Regression YouTube search...]
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* [http://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/neural-network-regression Neural Network Regression | Microsoft]
 
* [http://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/neural-network-regression Neural Network Regression | Microsoft]
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* [http://www.quora.com/When-should-we-use-logistic-regression-and-Neural-Network When should we use logistic regression and Neural Network? | Sebastian Raschka]
  
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Recently, several studies on calibrating traffic flow models have been undertaken using support vector regression (SVR). For me, the method seems to be a bit counter-intuitive. Rather than the sum of the squared errors, the sum of squared prefactors of the model function is minimized. However, this seems to have nothing to do with the fit quality itself. The fit quality enters only indirectly in form of some constraints, and small deviations are not penalized at all. Furthermore, you obtain a sort of black-box model which is lengthy to write down explicitly and which cannot be understood intuitively. Under which circumstances, the SVR should nevertheless be preferred to an ordinary LSE minimization? - Martin Treiber
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The simplest neural network performs least squares regression - single-layer neural network, with a single node that uses a linear activation function
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Revision as of 05:38, 2 June 2018

YouTube search...

The simplest neural network performs least squares regression - single-layer neural network, with a single node that uses a linear activation function

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