mlpack
2.2.5

Tutorial for how to perform density estimation with Density Estimation Trees (DET). More...
Functions  
f f  $V (t)\f$ is the volume of the node \f $t\f$ and \f$\tilde 
see subsection cli_alt_reg_tut Alternate DET regularization The usual regularized error f $R_  alpha (t)\f$ of a node \f $t\f$ is given by 
Variables  
f is the set of leaves in the subtree rooted at f $t f For the purposes of density  estimation 
this option is not available in DET right  now 
f is the set of leaves in the subtree rooted at f $t f For the purposes of density there is a different form of  regularization 
f is the set of leaves in the subtree rooted at f $t f For the purposes of density there is a different form of we penalize the sum of the inverse of the volumes of the leaves With this very small volume nodes are discouraged unless the data actually warrants it  Thus 
Detailed Description
Tutorial for how to perform density estimation with Density Estimation Trees (DET).
Definition in file det.txt.
Function Documentation
◆ $V()
◆ alpha()
see subsection cli_alt_reg_tut Alternate DET regularization The usual regularized error f $R_ alpha  (  t  ) 
Variable Documentation
◆ estimation
f is the set of leaves in the subtree rooted at f $t f For the purposes of density estimation 
◆ now
◆ regularization
f is the set of leaves in the subtree rooted at f $t f For the purposes of density there is a different form of we penalize the sum of the inverse of the volumes of the leaves With this regularization 
◆ Thus
f is the set of leaves in the subtree rooted at f $t f For the purposes of density there is a different form of we penalize the sum of the inverse of the volumes of the leaves With this very small volume nodes are discouraged unless the data actually warrants it Thus 
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