Approximation of the optimal two-part minimum description length (MDL) code for given data, through successive monotonically length-decreasing two-part MDL codes, has the following properties: (i) computation of each step may take arbitrarily long; (ii) we may not know when we reach the optimum, or whether we will reach the optimum at all; (iii) the sequence of models generated may not monotonically improve the goodness of fit; but (iv) the model associated with the optimum has (almost) the best goodness of fit. To express the practically interesting goodness of fit of individual models for individual data sets we have to rely on Kolmogorov complexity
This is about the Minimum Description Length (MDL) principle applied to pattern mining. The length o...
Model selection plays an important part in machine learning and in artificial intelligence in genera...
Common approximations for the minimum description length (MDL) criterion imply that the cost of addi...
Approximation of the optimal two-part minimum description length (MDL) code for given data, through ...
We point out a potential weakness in the application of the celebrated Minimum Description Length (M...
AbstractThe Minimum Description Length (MDL) principle is solidly based on a provably ideal method o...
The minimum description length(MDL) method is one of the pioneer methods of parametric order estima-...
The concept of overfitting in model selection is explained and demonstrated with an example. After p...
: Statistics based inference methods like minimum message length (MML) and minimum description lengt...
In the Minimum Description Length (MDL) principle, learning from the data is equivalent to an optima...
In the Minimum Description Length (MDL) principle, learning from the data is equivalent to an optima...
When considering a data set it is often unknown how complex it is, and hence it is difficult to asse...
cCorresponding Author The Minimum Description Length (MDL) principle is an information theoretic app...
Ignoring practicality, we investigate the ideal form of minimum description length induction where e...
Minimum Description Length (MDL) inference is based on the intuition that understanding the availabl...
This is about the Minimum Description Length (MDL) principle applied to pattern mining. The length o...
Model selection plays an important part in machine learning and in artificial intelligence in genera...
Common approximations for the minimum description length (MDL) criterion imply that the cost of addi...
Approximation of the optimal two-part minimum description length (MDL) code for given data, through ...
We point out a potential weakness in the application of the celebrated Minimum Description Length (M...
AbstractThe Minimum Description Length (MDL) principle is solidly based on a provably ideal method o...
The minimum description length(MDL) method is one of the pioneer methods of parametric order estima-...
The concept of overfitting in model selection is explained and demonstrated with an example. After p...
: Statistics based inference methods like minimum message length (MML) and minimum description lengt...
In the Minimum Description Length (MDL) principle, learning from the data is equivalent to an optima...
In the Minimum Description Length (MDL) principle, learning from the data is equivalent to an optima...
When considering a data set it is often unknown how complex it is, and hence it is difficult to asse...
cCorresponding Author The Minimum Description Length (MDL) principle is an information theoretic app...
Ignoring practicality, we investigate the ideal form of minimum description length induction where e...
Minimum Description Length (MDL) inference is based on the intuition that understanding the availabl...
This is about the Minimum Description Length (MDL) principle applied to pattern mining. The length o...
Model selection plays an important part in machine learning and in artificial intelligence in genera...
Common approximations for the minimum description length (MDL) criterion imply that the cost of addi...